workshop 10 multiple regression

*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, 21 Dec 2010 12:55:43 +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/21/t1292936109xtnhczit9bas1w7.htm/, Retrieved Tue, 21 Dec 2010 13:55:22 +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/21/t1292936109xtnhczit9bas1w7.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 «

162556 807 213118 6282154 29790 444 81767 4321023 87550 412 153198 4111912 84738 428 -26007 223193 54660 315 126942 1491348 42634 168 157214 1629616 40949 263 129352 1398893 45187 267 234817 1926517 37704 228 60448 983660 16275 129 47818 1443586 25830 104 245546 1073089 12679 122 48020 984885 18014 393 -1710 1405225 43556 190 32648 227132 24811 280 95350 929118 6575 63 151352 1071292 7123 102 288170 638830 21950 265 114337 856956 37597 234 37884 992426 17821 277 122844 444477 12988 73 82340 857217 22330 67 79801 711969 13326 103 165548 702380 16189 290 116384 358589 7146 83 134028 297978 15824 56 63838 585715 27664 236 74996 657954 11920 73 31080 209458 8568 34 32168 786690 14416 139 49857 439798 3369 26 87161 688779 11819 70 106113 574339 6984 40 80570 741409 4519 42 102129 597793 2220 12 301670 644190 18562 211 102313 377934 10327 74 88577 640273 5336 80 112477 697458 2365 83 191778 550608 4069 131 79804 207393 8636 203 128294 301607 13718 56 96448 345783 4525 89 93 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 27 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Orders[t] = -5.92658827283643 + 0.00479191603048801Costs[t] + 0.000213380207732667Dividends[t] + 1.89618540709206e-05Wealth[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) -5.92658827283643 4.329137 -1.369 0.171719 0.08586 Costs 0.00479191603048801 0.000234 20.4773 0 0 Dividends 0.000213380207732667 5.6e-05 3.7829 0.000177 8.9e-05 Wealth 1.89618540709206e-05 7e-06 2.8866 0.004092 0.002046 Multiple Linear Regression - Regression Statistics Multiple R 0.885374171063649 R-squared 0.783887422786644 Adjusted R-squared 0.782369067443224 F-TEST (value) 516.274023853543 F-TEST (DF numerator) 3 F-TEST (DF denominator) 427 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 34.7471610458232 Sum Squared Residuals 515544.940717848 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 807 937.624564489792 -130.624564489792 2 444 236.206657284166 207.793342715834 3 412 524.264556557086 -112.264556557086 4 428 398.813566351803 29.1864336481973 5 315 311.365175428597 3.63482457140286 6 168 262.81885653311 -94.81885653311 7 263 244.424342817085 18.5756571829150 8 267 297.241355855135 -30.2413558551345 9 228 206.298237913109 21.7017620868909 10 129 109.638326967540 19.3616730324595 11 104 190.591016305705 -86.5910163057046 12 122 83.7518782996822 38.2481217003178 13 393 106.675778331961 286.324221668039 14 190 214.063387211991 -24.0633872119912 15 280 150.929243097577 129.070756902423 16 63 78.1894633997217 -15.1894633997217 17 102 101.809405310779 0.190594689221014 18 265 139.902696025105 125.097303974895 19 234 201.137011503353 32.8629884966474 20 277 114.110733557083 162.889266442917 21 73 90.134967096962 -17.1349670969620 22 67 131.605102926254 -64.6051029262544 23 103 106.573578441508 -3.57357844150768 24 290 103.28329473093 186.71670526907 25 83 62.5655815153697 20.4344184846303 26 56 94.628699051993 -38.6286990519930 27 236 155.115666587081 80.8843334129188 28 73 61.7966196968987 11.2033803031013 29 34 56.9116637777817 -22.9116637777817 30 139 82.1315557362889 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15.0517082518089 -7.05170825180893 394 6 18.8930788490520 -12.8930788490520 395 9 33.7120370914658 -24.7120370914658 396 8 14.6171572699288 -6.61715726992884 397 26 33.5241407440228 -7.52414074402279 398 239 35.1539148318381 203.846085168162 399 7 15.8545870240823 -8.85458702408226 400 41 13.1152246982646 27.8847753017354 401 3 19.9519839877929 -16.9519839877929 402 8 17.8212224514139 -9.82122245141392 403 6 15.2613816663721 -9.26138166637205 404 21 21.4741740434027 -0.474174043402675 405 7 15.8746714367208 -8.87467143672084 406 11 21.0228158948232 -10.0228158948232 407 11 17.5718925947258 -6.57189259472583 408 12 17.1620397895716 -5.16203978957157 409 9 14.2690575146117 -5.26905751461167 410 3 16.8812041491677 -13.8812041491677 411 57 21.0918745780492 35.9081254219508 412 21 48.5633523272375 -27.5633523272375 413 15 16.9971725150812 -1.99717251508119 414 32 34.4867651701192 -2.48676517011915 415 11 23.3569131145756 -12.3569131145756 416 2 18.2533029958991 -16.2533029958991 417 23 24.918032186671 -1.91803218667099 418 20 18.3105861835014 1.68941381649858 419 24 20.1467427483496 3.8532572516504 420 1 15.9817659815525 -14.9817659815525 421 1 18.6213829872043 -17.6213829872043 422 74 27.7785883166275 46.2214116833725 423 68 44.6321458259422 23.3678541740578 424 20 36.4593633237323 -16.4593633237323 425 20 21.8664933108736 -1.86649331087360 426 82 36.9435559093310 45.056444090669 427 21 24.9831414487479 -3.9831414487479 428 244 204.148182564618 39.8518174353822 429 32 60.181610753642 -28.1816107536420 430 86 90.6729822987977 -4.67298229879773 431 69 86.3636192402627 -17.3636192402627 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.998234809936294 0.00353038012741148 0.00176519006370574 8 0.999810563098458 0.000378873803085073 0.000189436901542537 9 0.999740215894238 0.00051956821152422 0.00025978410576211 10 0.999983289665707 3.34206685858732e-05 1.67103342929366e-05 11 0.999984823024126 3.03539517479831e-05 1.51769758739915e-05 12 0.999984243932588 3.1512134824225e-05 1.57560674121125e-05 13 0.99999999999206 1.58823318595012e-11 7.94116592975059e-12 14 0.999999999999117 1.76613507449794e-12 8.83067537248972e-13 15 0.999999999999992 1.60103337299119e-14 8.00516686495595e-15 16 0.999999999999992 1.51206462736369e-14 7.56032313681844e-15 17 1 2.44781976520175e-16 1.22390988260088e-16 18 1 2.95926373530191e-18 1.47963186765095e-18 19 1 4.41338005152886e-18 2.20669002576443e-18 20 1 7.10471876745017e-23 3.55235938372508e-23 21 1 5.7379780903922e-25 2.8689890451961e-25 22 1 3.05128955936061e-29 1.52564477968030e-29 23 1 5.69924480128118e-29 2.84962240064059e-29 24 1 5.24880972446123e-37 2.62440486223062e-37 25 1 1.51959938295583e-36 7.59799691477914e-37 26 1 7.89591535622413e-40 3.94795767811207e-40 27 1 1.37550994526974e-39 6.87754972634871e-40 28 1 2.44589109392692e-40 1.22294554696346e-40 29 1 3.07564387335214e-43 1.53782193667607e-43 30 1 8.13018924412074e-43 4.06509462206037e-43 31 1 8.13901001374471e-44 4.06950500687236e-44 32 1 3.25205545513416e-44 1.62602772756708e-44 33 1 3.21219574729773e-45 1.60609787364887e-45 34 1 2.61257778044837e-45 1.30628889022419e-45 35 1 2.00580293602613e-47 1.00290146801307e-47 36 1 8.33735529483842e-49 4.16867764741921e-49 37 1 7.47138975931544e-49 3.73569487965772e-49 38 1 2.50466605909606e-48 1.25233302954803e-48 39 1 6.9090731301995e-48 3.45453656509975e-48 40 1 5.50564507832185e-50 2.75282253916092e-50 41 1 1.77732580345025e-55 8.88662901725124e-56 42 1 6.34381861739689e-57 3.17190930869844e-57 43 1 1.39960123680557e-56 6.99800618402784e-57 44 1 5.16156605693118e-56 2.58078302846559e-56 45 1 3.92550565077711e-56 1.96275282538855e-56 46 1 1.96723127067771e-56 9.83615635338854e-57 47 1 8.46307710404787e-57 4.23153855202393e-57 48 1 6.64308550777684e-58 3.32154275388842e-58 49 1 1.53724213489926e-57 7.6862106744963e-58 50 1 2.47398105743513e-57 1.23699052871756e-57 51 1 4.06606580446589e-57 2.03303290223295e-57 52 1 1.51595399475413e-58 7.57976997377063e-59 53 1 3.94854990283233e-58 1.97427495141617e-58 54 1 1.40094151391943e-57 7.00470756959713e-58 55 1 4.21111809874073e-58 2.10555904937036e-58 56 1 1.73884198920716e-58 8.6942099460358e-59 57 1 2.18862159177530e-60 1.09431079588765e-60 58 1 2.84818188795705e-60 1.42409094397853e-60 59 1 5.83449794910527e-60 2.91724897455264e-60 60 1 1.87616148160744e-59 9.3808074080372e-60 61 1 1.47223247635142e-59 7.36116238175708e-60 62 1 1.52291192419695e-59 7.61455962098476e-60 63 1 1.14952514860330e-59 5.74762574301649e-60 64 1 3.5913741579527e-59 1.79568707897635e-59 65 1 5.35159304939798e-59 2.67579652469899e-59 66 1 5.28766558614244e-71 2.64383279307122e-71 67 1 6.68556136217954e-71 3.34278068108977e-71 68 1 1.82503820973948e-70 9.12519104869742e-71 69 1 2.05513197792759e-70 1.02756598896380e-70 70 1 1.86757903982125e-70 9.33789519910624e-71 71 1 7.42975633543278e-71 3.71487816771639e-71 72 1 9.03134987939469e-71 4.51567493969734e-71 73 1 2.42053544593104e-70 1.21026772296552e-70 74 1 2.55888203674623e-70 1.27944101837311e-70 75 1 6.31310113460474e-70 3.15655056730237e-70 76 1 1.07985375736825e-69 5.39926878684127e-70 77 1 2.78526497257905e-69 1.39263248628952e-69 78 1 4.09692721440528e-69 2.04846360720264e-69 79 1 3.33132969465356e-69 1.66566484732678e-69 80 1 4.37515252781158e-69 2.18757626390579e-69 81 1 8.08161766612265e-69 4.04080883306133e-69 82 1 2.18496115725785e-68 1.09248057862892e-68 83 1 6.6349841488424e-68 3.3174920744212e-68 84 1 8.43848402427376e-68 4.21924201213688e-68 85 1 1.39280387954948e-67 6.96401939774741e-68 86 1 2.4494586098786e-67 1.2247293049393e-67 87 1 1.49726708141129e-68 7.48633540705647e-69 88 1 2.28547837342715e-68 1.14273918671358e-68 89 1 6.95108304085186e-68 3.47554152042593e-68 90 1 1.07363301020348e-67 5.36816505101739e-68 91 1 3.05461847235042e-67 1.52730923617521e-67 92 1 8.25202284143078e-67 4.12601142071539e-67 93 1 2.69337657632568e-67 1.34668828816284e-67 94 1 3.94451897950782e-68 1.97225948975391e-68 95 1 2.0886299973186e-68 1.0443149986593e-68 96 1 4.28368587787804e-68 2.14184293893902e-68 97 1 3.36359678511384e-68 1.68179839255692e-68 98 1 7.60838132316371e-68 3.80419066158186e-68 99 1 2.03708718159636e-68 1.01854359079818e-68 100 1 2.83150322450868e-68 1.41575161225434e-68 101 1 7.45711145374817e-68 3.72855572687408e-68 102 1 1.78934515108981e-67 8.94672575544907e-68 103 1 6.23796970045352e-67 3.11898485022676e-67 104 1 3.13824000615114e-67 1.56912000307557e-67 105 1 7.57824855267344e-67 3.78912427633672e-67 106 1 1.85824514873908e-66 9.29122574369542e-67 107 1 2.38256005658335e-66 1.19128002829167e-66 108 1 1.67898347280208e-66 8.39491736401039e-67 109 1 3.63488305198468e-66 1.81744152599234e-66 110 1 8.73822024617598e-66 4.36911012308799e-66 111 1 2.09357110529229e-65 1.04678555264614e-65 112 1 3.55620878934363e-65 1.77810439467182e-65 113 1 1.01786406531744e-64 5.08932032658721e-65 114 1 1.52891683364392e-64 7.64458416821961e-65 115 1 2.32926529666929e-64 1.16463264833465e-64 116 1 3.40610050454673e-64 1.70305025227337e-64 117 1 5.20374210395379e-64 2.60187105197690e-64 118 1 5.74208797248764e-64 2.87104398624382e-64 119 1 8.51668145217964e-64 4.25834072608982e-64 120 1 1.9977908918844e-63 9.988954459422e-64 121 1 6.16834712861427e-63 3.08417356430714e-63 122 1 1.38212570769498e-62 6.9106285384749e-63 123 1 3.82153184267664e-66 1.91076592133832e-66 124 1 4.8422842226676e-66 2.4211421113338e-66 125 1 1.00664189100455e-65 5.03320945502275e-66 126 1 2.37308211422996e-65 1.18654105711498e-65 127 1 4.43386634719849e-65 2.21693317359924e-65 128 1 1.07363725168442e-64 5.36818625842208e-65 129 1 2.10979580421703e-64 1.05489790210851e-64 130 1 4.07679945876484e-64 2.03839972938242e-64 131 1 9.72122439984863e-64 4.86061219992431e-64 132 1 2.15009590539141e-63 1.07504795269570e-63 133 1 4.45078622272992e-63 2.22539311136496e-63 134 1 9.86598541655797e-63 4.93299270827898e-63 135 1 2.07668137788452e-62 1.03834068894226e-62 136 1 4.63875564292600e-62 2.31937782146300e-62 137 1 1.17759215870516e-61 5.88796079352582e-62 138 1 2.53448680394992e-61 1.26724340197496e-61 139 1 5.24677882397741e-61 2.62338941198871e-61 140 1 1.10869396510078e-60 5.5434698255039e-61 141 1 2.75860945268596e-60 1.37930472634298e-60 142 1 5.91002855199899e-60 2.95501427599950e-60 143 1 1.49661589336219e-59 7.48307946681094e-60 144 1 3.61983685542457e-59 1.80991842771228e-59 145 1 9.7203888612894e-59 4.8601944306447e-59 146 1 2.45233988757918e-58 1.22616994378959e-58 147 1 5.37717944648044e-58 2.68858972324022e-58 148 1 1.38456472088595e-57 6.92282360442973e-58 149 1 3.10750000833887e-57 1.55375000416944e-57 150 1 6.86336652715282e-57 3.43168326357641e-57 151 1 1.74347566610564e-56 8.7173783305282e-57 152 1 1.41377956009012e-56 7.0688978004506e-57 153 1 3.57594594768825e-56 1.78797297384412e-56 154 1 9.1188552787193e-56 4.55942763935965e-56 155 1 2.16597408410390e-55 1.08298704205195e-55 156 1 4.89212923356338e-55 2.44606461678169e-55 157 1 1.10838371987778e-54 5.54191859938892e-55 158 1 2.61405924371962e-54 1.30702962185981e-54 159 1 6.36832156852283e-54 3.18416078426142e-54 160 1 1.45266995487522e-53 7.26334977437609e-54 161 1 3.32094604924863e-53 1.66047302462432e-53 162 1 7.60708723655404e-53 3.80354361827702e-53 163 1 1.74561858035052e-52 8.7280929017526e-53 164 1 4.47024887445388e-52 2.23512443722694e-52 165 1 9.92349920292482e-52 4.96174960146241e-52 166 1 2.32948614885633e-51 1.16474307442816e-51 167 1 5.35661856968898e-51 2.67830928484449e-51 168 1 1.00588299266067e-50 5.02941496330337e-51 169 1 1.36818124568571e-50 6.84090622842853e-51 170 1 3.15472863639478e-50 1.57736431819739e-50 171 1 7.27959094119862e-50 3.63979547059931e-50 172 1 1.78788112279391e-49 8.93940561396953e-50 173 1 4.12769576132851e-49 2.06384788066425e-49 174 1 1.08139271874425e-48 5.40696359372124e-49 175 1 2.49491741084623e-48 1.24745870542312e-48 176 1 6.46827727550871e-48 3.23413863775436e-48 177 1 5.12078927308147e-55 2.56039463654073e-55 178 1 1.25344828051824e-54 6.2672414025912e-55 179 1 3.10332931679821e-54 1.55166465839910e-54 180 1 7.68376504881011e-54 3.84188252440505e-54 181 1 2.24116361137165e-53 1.12058180568582e-53 182 1 6.03351755757645e-53 3.01675877878823e-53 183 1 1.50299868034252e-52 7.51499340171258e-53 184 1 4.36734022963868e-52 2.18367011481934e-52 185 1 1.17351145610884e-51 5.86755728054419e-52 186 1 3.21106714828239e-51 1.60553357414120e-51 187 1 9.02743920279372e-51 4.51371960139686e-51 188 1 2.42618321297856e-50 1.21309160648928e-50 189 1 6.23285084765044e-50 3.11642542382522e-50 190 1 1.52100781110506e-49 7.60503905552532e-50 191 1 4.07669589891722e-49 2.03834794945861e-49 192 1 9.9259906075307e-49 4.96299530376535e-49 193 1 2.66662514011145e-48 1.33331257005573e-48 194 1 6.47518518778447e-48 3.23759259389224e-48 195 1 1.66611263497586e-47 8.33056317487932e-48 196 1 4.03496192505144e-47 2.01748096252572e-47 197 1 5.04448957725076e-47 2.52224478862538e-47 198 1 1.22000129277103e-46 6.10000646385515e-47 199 1 2.94667151425359e-46 1.47333575712679e-46 200 1 8.21538730244708e-46 4.10769365122354e-46 201 1 1.97497033817323e-45 9.87485169086615e-46 202 1 4.74075520470578e-45 2.37037760235289e-45 203 1 1.13621194141645e-44 5.68105970708226e-45 204 1 2.48029538436169e-44 1.24014769218084e-44 205 1 4.8438114178909e-44 2.42190570894545e-44 206 1 1.25093674707174e-43 6.2546837353587e-44 207 1 2.91448909413641e-43 1.45724454706821e-43 208 1 6.92624010765067e-43 3.46312005382533e-43 209 1 8.82593414455457e-43 4.41296707227728e-43 210 1 2.17732030815514e-42 1.08866015407757e-42 211 1 5.16502462231835e-42 2.58251231115918e-42 212 1 1.22280488373966e-41 6.11402441869831e-42 213 1 3.02278665166282e-41 1.51139332583141e-41 214 1 7.82950643503619e-41 3.91475321751809e-41 215 1 1.90029114852110e-40 9.50145574260552e-41 216 1 4.63453989766342e-40 2.31726994883171e-40 217 1 1.17434272822083e-39 5.87171364110413e-40 218 1 2.95220135011220e-39 1.47610067505610e-39 219 1 7.13825060981613e-39 3.56912530490807e-39 220 1 1.31744256536668e-38 6.58721282683342e-39 221 1 3.16465229706981e-38 1.58232614853491e-38 222 1 7.32333557495351e-38 3.66166778747676e-38 223 1 1.72447643139934e-37 8.6223821569967e-38 224 1 3.67546790826598e-37 1.83773395413299e-37 225 1 8.59225503573986e-37 4.29612751786993e-37 226 1 1.67313497532791e-36 8.36567487663957e-37 227 1 4.11062780547336e-36 2.05531390273668e-36 228 1 9.3843284526578e-36 4.6921642263289e-36 229 1 2.06550679725664e-35 1.03275339862832e-35 230 1 4.69052553044052e-35 2.34526276522026e-35 231 1 1.12998491650061e-34 5.64992458250306e-35 232 1 2.72970057233053e-34 1.36485028616527e-34 233 1 6.1435848734371e-34 3.07179243671855e-34 234 1 1.46466618838024e-33 7.3233309419012e-34 235 1 3.27733024704943e-33 1.63866512352472e-33 236 1 7.30917072631797e-33 3.65458536315899e-33 237 1 1.62611333621280e-32 8.13056668106402e-33 238 1 3.85363431549492e-32 1.92681715774746e-32 239 1 8.52109246212984e-32 4.26054623106492e-32 240 1 1.87853826592921e-31 9.39269132964606e-32 241 1 4.12886052531367e-31 2.06443026265684e-31 242 1 9.3754177987665e-31 4.68770889938325e-31 243 1 2.04762108367869e-30 1.02381054183934e-30 244 1 4.75582540138898e-30 2.37791270069449e-30 245 1 5.00774669738699e-30 2.50387334869349e-30 246 1 1.08761213098283e-29 5.43806065491413e-30 247 1 2.35456340373811e-29 1.17728170186905e-29 248 1 5.18128271567583e-29 2.59064135783791e-29 249 1 1.11483315792834e-28 5.57416578964169e-29 250 1 2.39085333892216e-28 1.19542666946108e-28 251 1 5.14004363860766e-28 2.57002181930383e-28 252 1 1.09579253018530e-27 5.47896265092649e-28 253 1 2.23930202080812e-27 1.11965101040406e-27 254 1 3.26841135754302e-27 1.63420567877151e-27 255 1 3.78242914274922e-27 1.89121457137461e-27 256 1 8.01773971598919e-27 4.00886985799459e-27 257 1 1.77416428944891e-26 8.87082144724455e-27 258 1 1.72001338798137e-26 8.60006693990683e-27 259 1 3.63653444195537e-26 1.81826722097769e-26 260 1 7.6616595440602e-26 3.8308297720301e-26 261 1 1.60851356934201e-25 8.04256784671006e-26 262 1 3.36496776232945e-25 1.68248388116473e-25 263 1 7.44883297854506e-25 3.72441648927253e-25 264 1 1.61816182236191e-24 8.09080911180956e-25 265 1 3.34761503590771e-24 1.67380751795385e-24 266 1 6.90016938983736e-24 3.45008469491868e-24 267 1 1.41704135676248e-23 7.08520678381239e-24 268 1 2.73932281861096e-23 1.36966140930548e-23 269 1 5.58326960165559e-23 2.79163480082780e-23 270 1 1.03104039458292e-22 5.1552019729146e-23 271 1 2.08903318208909e-22 1.04451659104454e-22 272 1 4.21658944236068e-22 2.10829472118034e-22 273 1 8.47835546005043e-22 4.23917773002522e-22 274 1 1.56341658619619e-21 7.81708293098095e-22 275 1 3.1246297321886e-21 1.5623148660943e-21 276 1 6.54046869168723e-21 3.27023434584361e-21 277 1 1.17946384046544e-20 5.8973192023272e-21 278 1 2.3339907771185e-20 1.16699538855925e-20 279 1 4.30859984060138e-20 2.15429992030069e-20 280 1 8.7779714429378e-20 4.3889857214689e-20 281 1 1.71800363959293e-19 8.59001819796467e-20 282 1 3.34886009650247e-19 1.67443004825124e-19 283 1 4.43077907789652e-19 2.21538953894826e-19 284 1 8.61088233887128e-19 4.30544116943564e-19 285 1 1.69353543132355e-18 8.46767715661775e-19 286 1 3.26328398748072e-18 1.63164199374036e-18 287 1 6.4075200346868e-18 3.2037600173434e-18 288 1 1.25424462177876e-17 6.27122310889378e-18 289 1 2.38595339486368e-17 1.19297669743184e-17 290 1 4.33081312904271e-17 2.16540656452135e-17 291 1 8.1804112525482e-17 4.0902056262741e-17 292 1 1.60174507636877e-16 8.00872538184383e-17 293 1 3.11818878797958e-16 1.55909439398979e-16 294 1 5.81187246278491e-16 2.90593623139246e-16 295 1 1.11226848913519e-15 5.56134244567594e-16 296 0.999999999999999 2.14014715673791e-15 1.07007357836896e-15 297 0.999999999999998 3.93817584398754e-15 1.96908792199377e-15 298 0.999999999999996 7.21442833708648e-15 3.60721416854324e-15 299 0.999999999999993 1.31568222746619e-14 6.57841113733097e-15 300 0.999999999999988 2.48325186147578e-14 1.24162593073789e-14 301 0.999999999999977 4.63985790316949e-14 2.31992895158474e-14 302 0.999999999999958 8.4204751314591e-14 4.21023756572955e-14 303 0.999999999999924 1.52430235839104e-13 7.62151179195522e-14 304 0.999999999999864 2.71659705278077e-13 1.35829852639038e-13 305 0.999999999999752 4.95226922362221e-13 2.47613461181111e-13 306 0.999999999999554 8.92429165196976e-13 4.46214582598488e-13 307 0.999999999999215 1.56969837914969e-12 7.84849189574847e-13 308 0.99999999999865 2.69868137159103e-12 1.34934068579551e-12 309 0.99999999999784 4.31914443846627e-12 2.15957221923313e-12 310 0.999999999996243 7.51471269430358e-12 3.75735634715179e-12 311 0.999999999993495 1.30108880040795e-11 6.50544400203976e-12 312 0.999999999988539 2.29217186314002e-11 1.14608593157001e-11 313 0.999999999980347 3.93049480892956e-11 1.96524740446478e-11 314 0.999999999965706 6.85887910661232e-11 3.42943955330616e-11 315 0.999999999939782 1.20435883256748e-10 6.02179416283741e-11 316 0.999999999898296 2.03408187133727e-10 1.01704093566863e-10 317 0.999999999828556 3.42887489698554e-10 1.71443744849277e-10 318 0.999999999727282 5.45436644196202e-10 2.72718322098101e-10 319 0.999999999531602 9.36795107623622e-10 4.68397553811811e-10 320 0.999999999223272 1.55345580950646e-09 7.76727904753229e-10 321 0.99999999871869 2.56261817439845e-09 1.28130908719923e-09 322 0.999999997897415 4.20516967420905e-09 2.10258483710453e-09 323 0.999999996567966 6.86406765453378e-09 3.43203382726689e-09 324 0.999999994427736 1.11445272971520e-08 5.57226364857602e-09 325 0.99999999100135 1.79972992666599e-08 8.99864963332997e-09 326 0.999999985436082 2.91278351928237e-08 1.45639175964119e-08 327 0.99999997673607 4.65278607231484e-08 2.32639303615742e-08 328 0.999999963042439 7.39151226748087e-08 3.69575613374043e-08 329 0.999999941612236 1.16775528855100e-07 5.83877644275498e-08 330 0.999999908267853 1.83464294745858e-07 9.17321473729288e-08 331 0.99999985668714 2.86625718443498e-07 1.43312859221749e-07 332 0.999999777364081 4.45271837701677e-07 2.22635918850839e-07 333 0.999999656098375 6.8780325081374e-07 3.4390162540687e-07 334 0.999999459295306 1.08140938877368e-06 5.40704694386840e-07 335 0.999999168141922 1.66371615505912e-06 8.3185807752956e-07 336 0.999998716205563 2.56758887481389e-06 1.28379443740694e-06 337 0.999998066527606 3.86694478870484e-06 1.93347239435242e-06 338 0.999997096508278 5.80698344345886e-06 2.90349172172943e-06 339 0.999995640919622 8.71816075529148e-06 4.35908037764574e-06 340 0.99999335557982 1.32888403604296e-05 6.64442018021478e-06 341 0.999989911625763 2.01767484740991e-05 1.00883742370496e-05 342 0.999985206496707 2.95870065857005e-05 1.47935032928503e-05 343 0.99997843954973 4.31209005393889e-05 2.15604502696945e-05 344 0.999968770694012 6.24586119761482e-05 3.12293059880741e-05 345 0.99995384189555 9.23162089015729e-05 4.61581044507865e-05 346 0.999933960512202 0.000132078975595123 6.60394877975615e-05 347 0.999904365849806 0.000191268300387102 9.56341501935512e-05 348 0.999864914238315 0.000270171523369341 0.000135085761684671 349 0.999810406797978 0.000379186404044501 0.000189593202022250 350 0.999732633199247 0.000534733601505437 0.000267366800752719 351 0.99962431019004 0.000751379619921689 0.000375689809960844 352 0.999469985090534 0.00106002981893291 0.000530014909466457 353 0.999257093272168 0.00148581345566453 0.000742906727832266 354 0.998994404617953 0.00201119076409493 0.00100559538204747 355 0.998643712293693 0.00271257541261368 0.00135628770630684 356 0.998183001756922 0.00363399648615602 0.00181699824307801 357 0.997575449175932 0.00484910164813693 0.00242455082406846 358 0.996795883793574 0.00640823241285295 0.00320411620642648 359 0.99579460544541 0.00841078910918064 0.00420539455459032 360 0.994518373302945 0.0109632533941092 0.0054816266970546 361 0.992784919973811 0.0144301600523773 0.00721508002618863 362 0.990727161947524 0.0185456761049530 0.00927283805247652 363 0.988165818970471 0.0236683620590578 0.0118341810295289 364 0.984962844487317 0.0300743110253662 0.0150371555126831 365 0.98062616625715 0.0387476674856997 0.0193738337428499 366 0.975766050798185 0.0484678984036298 0.0242339492018149 367 0.969903776502706 0.0601924469945887 0.0300962234972944 368 0.96289191105739 0.0742161778852196 0.0371080889426098 369 0.954576020584094 0.0908479588318119 0.0454239794159059 370 0.943818216702332 0.112363566595336 0.056181783297668 371 0.932236955186311 0.135526089627378 0.067763044813689 372 0.91760263615039 0.164794727699221 0.0823973638496103 373 0.904355399643958 0.191289200712084 0.0956446003560421 374 0.887237260592828 0.225525478814344 0.112762739407172 375 0.867823414844887 0.264353170310226 0.132176585155113 376 0.846184070887191 0.307631858225617 0.153815929112808 377 0.81890670429238 0.362186591415239 0.181093295707619 378 0.790013712293954 0.419972575412092 0.209986287706046 379 0.761556117444786 0.476887765110428 0.238443882555214 380 0.730378037927423 0.539243924145155 0.269621962072577 381 0.697258827474928 0.605482345050144 0.302741172525072 382 0.657773548183841 0.684452903632318 0.342226451816159 383 0.62089068768049 0.758218624639021 0.379109312319511 384 0.582435400662738 0.835129198674524 0.417564599337262 385 0.536988731546094 0.926022536907811 0.463011268453906 386 0.490602108764204 0.981204217528408 0.509397891235796 387 0.447818235558219 0.895636471116437 0.552181764441781 388 0.405225322124769 0.810450644249539 0.59477467787523 389 0.363751763052393 0.727503526104786 0.636248236947607 390 0.321215147104213 0.642430294208426 0.678784852895787 391 0.280805340973511 0.561610681947023 0.719194659026489 392 0.243354909968130 0.486709819936259 0.75664509003187 393 0.208082919381421 0.416165838762842 0.791917080618579 394 0.183117579314494 0.366235158628988 0.816882420685506 395 0.184739696424223 0.369479392848447 0.815260303575777 396 0.154296613245182 0.308593226490363 0.845703386754818 397 0.126318799273105 0.252637598546210 0.873681200726895 398 0.999822689880478 0.000354620239044761 0.000177310119522381 399 0.999682309282818 0.000635381434363232 0.000317690717181616 400 0.999748048164002 0.000503903671996879 0.000251951835998439 401 0.999602599318763 0.000794801362474138 0.000397400681237069 402 0.999298330235892 0.00140333952821618 0.000701669764108089 403 0.998770211626777 0.00245957674644536 0.00122978837322268 404 0.997837177565714 0.00432564486857224 0.00216282243428612 405 0.99633608227018 0.00732783545963869 0.00366391772981935 406 0.994050179840855 0.0118996403182907 0.00594982015914535 407 0.990217032925066 0.0195659341498675 0.00978296707493373 408 0.984147763187354 0.0317044736252919 0.0158522368126459 409 0.974877133820114 0.0502457323597728 0.0251228661798864 410 0.963643890415565 0.0727122191688703 0.0363561095844351 411 0.973850022605876 0.0522999547882473 0.0261499773941236 412 0.974516759441528 0.0509664811169434 0.0254832405584717 413 0.95852765236453 0.0829446952709423 0.0414723476354712 414 0.935171262805059 0.129657474389882 0.0648287371949412 415 0.905094554223956 0.189810891552088 0.094905445776044 416 0.872189238056671 0.255621523886657 0.127810761943329 417 0.813638423659036 0.372723152681928 0.186361576340964 418 0.737329729216039 0.525340541567923 0.262670270783961 419 0.644410527820247 0.711178944359506 0.355589472179753 420 0.561400777735627 0.877198444528746 0.438599222264373 421 0.499546682435657 0.999093364871314 0.500453317564343 422 0.561600360591515 0.87679927881697 0.438399639408485 423 0.603877671439998 0.792244657120003 0.396122328560001 424 0.481152717823065 0.96230543564613 0.518847282176935 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 361 0.863636363636364 NOK 5% type I error level 371 0.88755980861244 NOK 10% type I error level 379 0.906698564593301 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/10w54u1292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/10w54u1292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/1plp11292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/1plp11292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/2plp11292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/2plp11292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/3plp11292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/3plp11292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/4hd6m1292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/4hd6m1292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/5hd6m1292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/5hd6m1292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/6sm571292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/6sm571292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/7lv5a1292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/7lv5a1292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/8lv5a1292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/8lv5a1292936115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/9lv5a1292936115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936109xtnhczit9bas1w7/9lv5a1292936115.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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