Paper - werkloosheid Brussel - MPR (2)

*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: Fri, 12 Dec 2008 02:35:33 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x.htm/, Retrieved Fri, 12 Dec 2008 09:37:48 +0000

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/2008/Dec/12/t1229074658mup7j8gq4ncsg5x.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

51.220 50.487 49.415 49.398 48.196 47.348 49.331 49.644 49.588 49.567 49.010 49.563 49.741 49.487 48.278 47.478 46.985 45.216 46.581 49.266 48.121 46.412 46.285 46.824 46.949 45.355 44.924 45.059 44.202 44.149 46.151 47.703 48.436 47.089 47.492 49.295 49.127 50.041 48.857 48.428 48.788 48.820 50.743 52.590 51.959 53.451 55.674 56.120 55.685 56.714 54.882 55.173 53.574 53.954 58.055 61.062 58.353 59.693 58.833 60.417 61.696 62.515 62.687 61.794 63.014 63.134 68.057 67.327 68.310 69.780 69.944 69.881 71.397 70.631 70.452 69.862 69.114 69.358 71.133 73.128 73.528 73.677 72.273 71.962 73.654 73.305 73.355 73.346 72.881 72.424 74.540 74.847 75.904 76.870 76.370 77.631 78.335 77.926 77.236 76.755 74.710 73.486 76.034 76.389 77.767 78.124 76.696 77.375 77.431 77.347 77.013 76.666 75.225 75.579 77.100 78.592 79.502 78.528 77.775 77.271 78.738 77.885 76.896 75.813 74.958 75.340 77.187 78.602 81.653 78.125 76.092 74.870 75.615 74.776 72.528 71.894 71.641 71.145 73.320 72.186 72.854 74.243 74.628 72.368 75.361 72.746 70.536 69.410 66.219 66.739 67.626 70.602 71.758 71.786 69.641 68.055 70.148 69.390 68.562 68.622 68.120 68.308 70.421 69.766 72.157 72.928 75.340 74.812 74.593 76.003 75.112 75.452 75.634 75.653 78.645 73.100 79.699 82.848 81.834 81.736 82.267 84.120 83.819 82.734 81.842 81.735 83.227 81.934 89.521 88.827 85.874 85.211 87.130 88.620 89.563 89.056 88.542 89.504 89.428 86.040 96.240 94.423 93.028 92.285 91.685 94.260 93.858 92.437 92.980 92.099 92.803 88.551 98.334 98.329 96.455 97.109 97.687 98.512 98.673 96.028 98.014 95.580 97.838 97.760 99.913 97.588 93.942 93.656 93.365 92.881 93.120 91.063 90.930 91.946 94.624 95.484 95.862 95.530 94.574 94.677 93.845 91.533 91.214 90.922 89.563 89.945 91.850 92.505 92.437 93.876

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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Werkloosheid[t] = + 48.425738372093 + 0.568423874123306M1[t] + 0.314930527870066M2[t] -0.529943770764118M3[t] -1.37653235511260M4[t] -2.15969236803248M5[t] -2.53389999999999M6[t] -0.674679060538942M7[t] -0.760600978220746M8[t] + 1.17414377076412M9[t] + 0.965078995939458M10[t] + 0.231545727205609M11[t] + 0.199445727205611t + 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) 48.425738372093 1.486566 32.5756 0 0 M1 0.568423874123306 1.862372 0.3052 0.76047 0.380235 M2 0.314930527870066 1.862306 0.1691 0.865856 0.432928 M3 -0.529943770764118 1.862255 -0.2846 0.776221 0.388111 M4 -1.37653235511260 1.862218 -0.7392 0.460523 0.230262 M5 -2.15969236803248 1.862196 -1.1598 0.247315 0.123658 M6 -2.53389999999999 1.862189 -1.3607 0.174898 0.087449 M7 -0.674679060538942 1.862196 -0.3623 0.717449 0.358724 M8 -0.760600978220746 1.862218 -0.4084 0.683321 0.34166 M9 1.17414377076412 1.862255 0.6305 0.528978 0.264489 M10 0.965078995939458 1.862306 0.5182 0.60479 0.302395 M11 0.231545727205609 1.884769 0.1229 0.902329 0.451165 t 0.199445727205611 0.005227 38.1587 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.927906471271435 R-squared 0.861010419427406 Adjusted R-squared 0.853972972309807 F-TEST (value) 122.346982512187 F-TEST (DF numerator) 12 F-TEST (DF denominator) 237 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.96014019568952 Sum Squared Residuals 8419.01526308893 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 51.22 49.1936079734217 2.02639202657826 2 50.487 49.1395603543744 1.34743964562565 3 49.415 48.4941317829458 0.920868217054252 4 49.398 47.8469889258029 1.55101107419711 5 48.196 47.2632746400886 0.932725359911435 6 47.348 47.0885127353267 0.259487264673294 7 49.331 49.1471794019934 0.183820598006611 8 49.644 49.2607032115172 0.383296788482810 9 49.588 51.3948936877077 -1.80689368770765 10 49.567 51.3852746400886 -1.8182746400886 11 49.01 50.8511870985603 -1.84118709856035 12 49.563 50.8190870985603 -1.25608709856035 13 49.741 51.5869566998894 -1.8459566998894 14 49.487 51.5329090808416 -2.04590908084163 15 48.278 50.8874805094131 -2.60948050941307 16 47.478 50.2403376522702 -2.76233765227021 17 46.985 49.6566233665559 -2.67162336655592 18 45.216 49.481861461794 -4.26586146179402 19 46.581 51.5405281284607 -4.95952812846068 20 49.266 51.6540519379845 -2.38805193798450 21 48.121 53.788242414175 -5.66724241417497 22 46.412 53.7786233665559 -7.36662336655592 23 46.285 53.2445358250277 -6.95953582502769 24 46.824 53.2124358250277 -6.38843582502768 25 46.949 53.9803054263566 -7.0313054263566 26 45.355 53.926257807309 -8.57125780730897 27 44.924 53.2808292358804 -8.3568292358804 28 45.059 52.6336863787375 -7.57468637873754 29 44.202 52.0499720930232 -7.84797209302325 30 44.149 51.8752101882614 -7.72621018826135 31 46.151 53.933876854928 -7.78287685492801 32 47.703 54.0474006644518 -6.34440066445182 33 48.436 56.1815911406423 -7.7455911406423 34 47.089 56.1719720930233 -9.08297209302326 35 47.492 55.637884551495 -8.14588455149502 36 49.295 55.605784551495 -6.31078455149502 37 49.127 56.3736541528239 -7.24665415282392 38 50.041 56.3196065337763 -6.2786065337763 39 48.857 55.6741779623477 -6.81717796234773 40 48.428 55.0270351052049 -6.59903510520487 41 48.788 54.4433208194906 -5.65532081949059 42 48.82 54.2685589147287 -5.44855891472868 43 50.743 56.3272255813953 -5.58422558139534 44 52.59 56.4407493909192 -3.85074939091916 45 51.959 58.5749398671096 -6.61593986710963 46 53.451 58.5653208194906 -5.11432081949059 47 55.674 58.0312332779624 -2.35723327796235 48 56.12 57.9991332779624 -1.87913327796235 49 55.685 58.7670028792913 -3.08200287929125 50 56.714 58.7129552602436 -1.99895526024363 51 54.882 58.0675266888151 -3.18552668881507 52 55.173 57.4203838316722 -2.2473838316722 53 53.574 56.8366695459579 -3.26266954595792 54 53.954 56.661907641196 -2.70790764119602 55 58.055 58.7205743078627 -0.665574307862675 56 61.062 58.8340981173865 2.22790188261351 57 58.353 60.968288593577 -2.61528859357696 58 59.693 60.9586695459579 -1.26566954595792 59 58.833 60.4245820044297 -1.59158200442968 60 60.417 60.3924820044297 0.0245179955703218 61 61.696 61.1603516057586 0.535648394241419 62 62.515 61.106303986711 1.40869601328904 63 62.687 60.4608754152824 2.22612458471761 64 61.794 59.8137325581395 1.98026744186046 65 63.014 59.2300182724253 3.78398172757475 66 63.134 59.0552563676633 4.07874363233666 67 68.057 61.11392303433 6.94307696567 68 67.327 61.2274468438538 6.09955315614618 69 68.31 63.3616373200443 4.94836267995571 70 69.78 63.3520182724252 6.42798172757475 71 69.944 62.817930730897 7.12606926910299 72 69.881 62.785830730897 7.09516926910299 73 71.397 63.5537003322259 7.8432996677741 74 70.631 63.4996527131783 7.13134728682171 75 70.452 62.8542241417497 7.59777585825028 76 69.862 62.2070812846069 7.65491871539313 77 69.114 61.6233669988926 7.49063300110742 78 69.358 61.4486050941307 7.90939490586934 79 71.133 63.5072717607973 7.62572823920266 80 73.128 63.6207955703211 9.50720442967885 81 73.528 65.7549860465116 7.77301395348838 82 73.677 65.7453669988926 7.93163300110743 83 72.273 65.2112794573643 7.06172054263565 84 71.962 65.1791794573643 6.78282054263566 85 73.654 65.9470490586932 7.70695094130675 86 73.305 65.8930014396456 7.41199856035438 87 73.355 65.247572868217 8.10742713178296 88 73.346 64.6004300110742 8.74556998892580 89 72.881 64.0167157253599 8.86428427464008 90 72.424 63.841953820598 8.582046179402 91 74.54 65.9006204872647 8.63937951273533 92 74.847 66.0141442967885 8.83285570321151 93 75.904 68.148334772979 7.75566522702104 94 76.87 68.1387157253599 8.73128427464009 95 76.37 67.6046281838317 8.76537181616833 96 77.631 67.5725281838317 10.0584718161683 97 78.335 68.3403977851606 9.99460221483942 98 77.926 68.286350166113 9.63964983388705 99 77.236 67.6409215946844 9.59507840531562 100 76.755 66.9937787375415 9.76122126245846 101 74.71 66.4100644518273 8.29993554817275 102 73.486 66.2353025470653 7.25069745293467 103 76.034 68.293969213732 7.740030786268 104 76.389 68.4074930232558 7.98150697674418 105 77.767 70.5416834994463 7.2253165005537 106 78.124 70.5320644518272 7.59193554817275 107 76.696 69.997976910299 6.698023089701 108 77.375 69.965876910299 7.409123089701 109 77.431 70.7337465116279 6.69725348837209 110 77.347 70.6796988925803 6.66730110741971 111 77.013 70.0342703211517 6.97872967884829 112 76.666 69.3871274640089 7.27887253599114 113 75.225 68.8034131782946 6.42158682170542 114 75.579 68.6286512735327 6.95034872646733 115 77.1 70.6873179401993 6.41268205980066 116 78.592 70.8008417497231 7.79115825027685 117 79.502 72.9350322259136 6.56696777408637 118 78.528 72.9254131782946 5.60258682170543 119 77.775 72.3913256367663 5.38367436323367 120 77.271 72.3592256367663 4.91177436323366 121 78.738 73.1270952380952 5.61090476190476 122 77.885 73.0730476190476 4.81195238095239 123 76.896 72.427619047619 4.46838095238096 124 75.813 71.7804761904762 4.03252380952381 125 74.958 71.196761904762 3.76123809523809 126 75.34 71.022 4.31800000000001 127 77.187 73.0806666666667 4.10633333333333 128 78.602 73.1941904761905 5.40780952380953 129 81.653 75.328380952381 6.32461904761905 130 78.125 75.3187619047619 2.80623809523809 131 76.092 74.7846743632337 1.30732563676633 132 74.87 74.7525743632337 0.117425636766336 133 75.615 75.5204439645626 0.094556035437425 134 74.776 75.466396345515 -0.690396345514953 135 72.528 74.8209677740864 -2.29296777408637 136 71.894 74.1738249169435 -2.27982491694351 137 71.641 73.5901106312292 -1.94911063122923 138 71.145 73.4153487264673 -2.27034872646733 139 73.32 75.474015393134 -2.15401539313400 140 72.186 75.5875392026578 -3.4015392026578 141 72.854 77.7217296788483 -4.86772967884829 142 74.243 77.7121106312292 -3.46911063122924 143 74.628 77.178023089701 -2.550023089701 144 72.368 77.145923089701 -4.777923089701 145 75.361 77.9137926910299 -2.55279269102990 146 72.746 77.8597450719823 -5.11374507198228 147 70.536 77.2143165005537 -6.6783165005537 148 69.41 76.5671736434109 -7.15717364341085 149 66.219 75.9834593576966 -9.76445935769658 150 66.739 75.8086974529347 -9.06969745293466 151 67.626 77.8673641196013 -10.2413641196013 152 70.602 77.9808879291251 -7.37888792912513 153 71.758 80.1150784053156 -8.35707840531562 154 71.786 80.1054593576966 -8.31945935769657 155 69.641 79.5713718161683 -9.93037181616832 156 68.055 79.5392718161683 -11.4842718161683 157 70.148 80.3071414174972 -10.1591414174972 158 69.39 80.2530937984496 -10.8630937984496 159 68.562 79.607665227021 -11.0456652270210 160 68.622 78.9605223698782 -10.3385223698782 161 68.12 78.376808084164 -10.2568080841639 162 68.308 78.202046179402 -9.89404617940199 163 70.421 80.2607128460687 -9.83971284606865 164 69.766 80.3742366555925 -10.6082366555925 165 72.157 82.508427131783 -10.3514271317829 166 72.928 82.498808084164 -9.5708080841639 167 75.34 81.9647205426357 -6.62472054263565 168 74.812 81.9326205426357 -7.12062054263566 169 74.593 82.7004901439646 -8.10749014396456 170 76.003 82.646442524917 -6.64344252491694 171 75.112 82.0010139534884 -6.88901395348837 172 75.452 81.3538710963455 -5.90187109634551 173 75.634 80.7701568106312 -5.13615681063123 174 75.653 80.5953949058693 -4.94239490586931 175 78.645 82.654061572536 -4.00906157253599 176 73.1 82.7675853820598 -9.6675853820598 177 79.699 84.9017758582503 -5.20277585825028 178 82.848 84.8921568106312 -2.04415681063123 179 81.834 84.358069269103 -2.52406926910298 180 81.736 84.325969269103 -2.58996926910299 181 82.267 85.0938388704319 -2.8268388704319 182 84.12 85.0397912513843 -0.919791251384267 183 83.819 84.3943626799557 -0.575362679955692 184 82.734 83.7472198228128 -1.01321982281285 185 81.842 83.1635055370986 -1.32150553709856 186 81.735 82.9887436323367 -1.25374363233665 187 83.227 85.0474102990033 -1.82041029900332 188 81.934 85.1609341085271 -3.22693410852713 189 89.521 87.2951245847176 2.22587541528239 190 88.827 87.2855055370986 1.54149446290144 191 85.874 86.7514179955703 -0.87741799557032 192 85.211 86.7193179955703 -1.50831799557032 193 87.13 87.4871875968992 -0.357187596899228 194 88.62 87.4331399778516 1.18686002214840 195 89.563 86.787711406423 2.77528859357697 196 89.056 86.1405685492802 2.91543145071982 197 88.542 85.556854263566 2.98514573643411 198 89.504 85.382092358804 4.12190764119601 199 89.428 87.4407590254707 1.98724097452934 200 86.04 87.5542828349945 -1.51428283499445 201 96.24 89.688473311185 6.55152668881506 202 94.423 89.6788542635659 4.74414573643411 203 93.028 89.1447667220376 3.88323327796236 204 92.285 89.1126667220377 3.17233327796234 205 91.685 89.8805363233666 1.80446367663345 206 94.26 89.826488704319 4.43351129568107 207 93.858 89.1810601328904 4.67693986710963 208 92.437 88.5339172757475 3.90308272425249 209 92.98 87.9502029900332 5.02979700996678 210 92.099 87.7754410852713 4.32355891472869 211 92.803 89.834107751938 2.96889224806201 212 88.551 89.9476315614618 -1.39663156146179 213 98.334 92.0818220376523 6.25217796234773 214 98.329 92.0722029900332 6.25679700996677 215 96.455 91.538115448505 4.91688455149502 216 97.109 91.506015448505 5.60298455149501 217 97.687 92.2738850498339 5.41311495016610 218 98.512 92.2198374307863 6.29216256921373 219 98.673 91.5744088593577 7.0985911406423 220 96.028 90.9272660022148 5.10073399778517 221 98.014 90.3435517165006 7.67044828349944 222 95.58 90.1687898117386 5.41121018826135 223 97.838 92.2274564784053 5.61054352159468 224 97.76 92.3409802879291 5.41901971207088 225 99.913 94.4751707641196 5.43782923588040 226 97.588 94.4655517165006 3.12244828349944 227 93.942 93.9314641749723 0.0105358250276773 228 93.656 93.8993641749723 -0.243364174972309 229 93.365 94.6672337763012 -1.30223377630123 230 92.881 94.6131861572536 -1.7321861572536 231 93.12 93.967757585825 -0.847757585825028 232 91.063 93.3206147286822 -2.25761472868217 233 90.93 92.7369004429679 -1.80690044296788 234 91.946 92.562138538206 -0.616138538205987 235 94.624 94.6208052048726 0.00319479512734987 236 95.484 94.7343290143965 0.749670985603538 237 95.862 96.868519490587 -1.00651949058694 238 95.53 96.8589004429679 -1.32890044296788 239 94.574 96.3248129014396 -1.75081290143965 240 94.677 96.2927129014396 -1.61571290143964 241 93.845 97.0605825027686 -3.21558250276856 242 91.533 97.006534883721 -5.47353488372093 243 91.214 96.3611063122924 -5.14710631229237 244 90.922 95.7139634551495 -4.79196345514951 245 89.563 95.1302491694352 -5.56724916943522 246 89.945 94.9554872646733 -5.01048726467333 247 91.85 97.01415393134 -5.16415393133999 248 92.505 97.1276777408638 -4.62267774086379 249 92.437 99.2618682170543 -6.82486821705427 250 93.876 99.2522491694352 -5.37624916943521 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000144797017174133 0.000289594034348265 0.999855202982826 17 6.08254682860727e-06 1.21650936572145e-05 0.999993917453171 18 1.16873062069684e-06 2.33746124139369e-06 0.99999883126938 19 6.76457352386192e-07 1.35291470477238e-06 0.999999323542648 20 2.23383357437078e-07 4.46766714874155e-07 0.999999776616643 21 1.74669323328613e-08 3.49338646657225e-08 0.999999982533068 22 1.20045441314760e-08 2.40090882629520e-08 0.999999987995456 23 2.31961515821317e-09 4.63923031642634e-09 0.999999997680385 24 4.05983650034609e-10 8.11967300069217e-10 0.999999999594016 25 6.12175647264741e-11 1.22435129452948e-10 0.999999999938782 26 4.61972429464696e-11 9.23944858929391e-11 0.999999999953803 27 7.32718715590978e-12 1.46543743118196e-11 0.999999999992673 28 8.28677350143105e-13 1.65735470028621e-12 0.999999999999171 29 9.24392436530327e-14 1.84878487306065e-13 0.999999999999908 30 2.88861208043080e-14 5.77722416086159e-14 0.999999999999971 31 1.28176284862565e-14 2.56352569725129e-14 0.999999999999987 32 4.65960255068361e-15 9.31920510136722e-15 0.999999999999995 33 1.90983642060422e-14 3.81967284120844e-14 0.99999999999998 34 1.41376894484556e-14 2.82753788969112e-14 0.999999999999986 35 2.47817516908632e-14 4.95635033817264e-14 0.999999999999975 36 1.99313724042304e-13 3.98627448084607e-13 0.9999999999998 37 3.24412091357235e-13 6.48824182714469e-13 0.999999999999676 38 3.30785357759214e-12 6.61570715518428e-12 0.999999999996692 39 7.65352052839338e-12 1.53070410567868e-11 0.999999999992346 40 9.06024223469985e-12 1.81204844693997e-11 0.99999999999094 41 2.38920090911622e-11 4.77840181823244e-11 0.999999999976108 42 9.3149350100279e-11 1.86298700200558e-10 0.99999999990685 43 2.40711916283447e-10 4.81423832566895e-10 0.999999999759288 44 5.12911795869244e-10 1.02582359173849e-09 0.999999999487088 45 6.90198317263373e-10 1.38039663452675e-09 0.999999999309802 46 4.19512255298033e-09 8.39024510596066e-09 0.999999995804878 47 7.2664130261574e-08 1.45328260523148e-07 0.99999992733587 48 3.36461220887083e-07 6.72922441774165e-07 0.99999966353878 49 5.77937842618721e-07 1.15587568523744e-06 0.999999422062157 50 1.43557948456632e-06 2.87115896913264e-06 0.999998564420515 51 2.01575398697870e-06 4.03150797395741e-06 0.999997984246013 52 2.89791280117949e-06 5.79582560235898e-06 0.9999971020872 53 2.84024944092754e-06 5.68049888185508e-06 0.999997159750559 54 3.43852900442755e-06 6.8770580088551e-06 0.999996561470996 55 7.55728776010646e-06 1.51145755202129e-05 0.99999244271224 56 2.07767767274927e-05 4.15535534549854e-05 0.999979223223272 57 2.53095988212846e-05 5.06191976425691e-05 0.999974690401179 58 4.32400436242988e-05 8.64800872485977e-05 0.999956759956376 59 4.78539104834393e-05 9.57078209668786e-05 0.999952146089517 60 5.58893993038668e-05 0.000111778798607734 0.999944110600696 61 6.47747662774956e-05 0.000129549532554991 0.999935225233723 62 8.40619707800325e-05 0.000168123941560065 0.99991593802922 63 0.000133826854007929 0.000267653708015857 0.999866173145992 64 0.000160239087383937 0.000320478174767873 0.999839760912616 65 0.000268128486804406 0.000536256973608813 0.999731871513196 66 0.00044535570222937 0.00089071140445874 0.99955464429777 67 0.00119958686781267 0.00239917373562535 0.998800413132187 68 0.00150579894583276 0.00301159789166551 0.998494201054167 69 0.00239342685972917 0.00478685371945834 0.99760657314027 70 0.00445983928169638 0.00891967856339276 0.995540160718304 71 0.00715435641967643 0.0143087128393529 0.992845643580324 72 0.0089574525432365 0.017914905086473 0.991042547456763 73 0.0106591679462065 0.0213183358924130 0.989340832053794 74 0.0109942189571453 0.0219884379142906 0.989005781042855 75 0.0118709716087786 0.0237419432175572 0.988129028391221 76 0.0120566182110558 0.0241132364221117 0.987943381788944 77 0.0116039391290604 0.0232078782581208 0.98839606087094 78 0.0115682394796414 0.0231364789592829 0.988431760520359 79 0.0105012852142014 0.0210025704284029 0.989498714785799 80 0.0102792093583131 0.0205584187166262 0.989720790641687 81 0.0100852085479096 0.0201704170958192 0.98991479145209 82 0.00970844694966018 0.0194168938993204 0.99029155305034 83 0.0082264625712416 0.0164529251424832 0.991773537428758 84 0.00656594344667734 0.0131318868933547 0.993434056553323 85 0.00527187047606787 0.0105437409521357 0.994728129523932 86 0.00414146172204721 0.00828292344409442 0.995858538277953 87 0.00338971111630899 0.00677942223261798 0.99661028888369 88 0.0028630676748973 0.0057261353497946 0.997136932325103 89 0.00242367009732718 0.00484734019465436 0.997576329902673 90 0.00200699727868176 0.00401399455736353 0.997993002721318 91 0.00164478573300506 0.00328957146601013 0.998355214266995 92 0.00135480687024779 0.00270961374049557 0.998645193129752 93 0.00106857942398837 0.00213715884797674 0.998931420576012 94 0.00090326388469786 0.00180652776939572 0.999096736115302 95 0.000760183750786871 0.00152036750157374 0.999239816249213 96 0.000719043314291803 0.00143808662858361 0.999280956685708 97 0.000654309069140729 0.00130861813828146 0.99934569093086 98 0.000583607130995585 0.00116721426199117 0.999416392869004 99 0.000524340061097558 0.00104868012219512 0.999475659938902 100 0.000487502005267427 0.000975004010534854 0.999512497994733 101 0.000418192646197485 0.00083638529239497 0.999581807353803 102 0.000346722379498329 0.000693444758996658 0.999653277620502 103 0.000302188143667405 0.00060437628733481 0.999697811856333 104 0.000297068205859661 0.000594136411719322 0.99970293179414 105 0.000244626333615316 0.000489252667230631 0.999755373666385 106 0.000209907271994381 0.000419814543988762 0.999790092728006 107 0.000182990597159063 0.000365981194318127 0.999817009402841 108 0.000175723278713171 0.000351446557426342 0.999824276721287 109 0.000179822839176827 0.000359645678353655 0.999820177160823 110 0.000183225660708708 0.000366451321417415 0.999816774339291 111 0.000187839089994979 0.000375678179989959 0.999812160910005 112 0.000205679869140397 0.000411359738280794 0.99979432013086 113 0.000224016507109642 0.000448033014219285 0.99977598349289 114 0.000243278263783837 0.000486556527567675 0.999756721736216 115 0.000283696118332234 0.000567392236664467 0.999716303881668 116 0.000422819911032641 0.000845639822065282 0.999577180088967 117 0.000458120888271493 0.000916241776542986 0.999541879111729 118 0.000512238040954628 0.00102447608190926 0.999487761959045 119 0.000602631669895786 0.00120526333979157 0.999397368330104 120 0.000809494239773852 0.00161898847954770 0.999190505760226 121 0.00117223089412234 0.00234446178824468 0.998827769105878 122 0.00167253359082535 0.00334506718165069 0.998327466409175 123 0.00237618841605454 0.00475237683210908 0.997623811583946 124 0.00358349961575378 0.00716699923150755 0.996416500384246 125 0.00522657011787409 0.0104531402357482 0.994773429882126 126 0.00743534504678496 0.0148706900935699 0.992564654953215 127 0.0112523364326182 0.0225046728652364 0.988747663567382 128 0.0219746585607665 0.0439493171215331 0.978025341439233 129 0.0337959705792663 0.0675919411585326 0.966204029420734 130 0.0457508604659251 0.0915017209318503 0.954249139534075 131 0.0622068628918074 0.124413725783615 0.937793137108193 132 0.0910749312236356 0.182149862447271 0.908925068776364 133 0.132400554329427 0.264801108658853 0.867599445670573 134 0.181282912674436 0.362565825348873 0.818717087325564 135 0.243962789346351 0.487925578692702 0.756037210653649 136 0.314634469614667 0.629268939229334 0.685365530385333 137 0.378851826212928 0.757703652425856 0.621148173787072 138 0.440489689447536 0.880979378895071 0.559510310552464 139 0.50541481311675 0.9891703737665 0.49458518688325 140 0.602063342299954 0.795873315400091 0.397936657700046 141 0.658494008690827 0.683011982618347 0.341505991309173 142 0.692087430689377 0.615825138621246 0.307912569310623 143 0.716694538711884 0.566610922576231 0.283305461288116 144 0.755128173459073 0.489743653081855 0.244871826540927 145 0.782671929351941 0.434656141296118 0.217328070648059 146 0.811140305200997 0.377719389598005 0.188859694799003 147 0.841957037723112 0.316085924553776 0.158042962276888 148 0.870127331986687 0.259745336026626 0.129872668013313 149 0.909789678783112 0.180420642433776 0.090210321216888 150 0.932101776866122 0.135796446267757 0.0678982231338785 151 0.953737535752502 0.0925249284949954 0.0462624642474977 152 0.96117439901099 0.0776512019780181 0.0388256009890090 153 0.966659837621972 0.0666803247560557 0.0333401623780279 154 0.970787057294254 0.0584258854114928 0.0292129427057464 155 0.978319626115593 0.0433607477688145 0.0216803738844072 156 0.986740187495962 0.0265196250080762 0.0132598125040381 157 0.99016756111744 0.0196648777651197 0.00983243888255985 158 0.99353639292564 0.0129272141487203 0.00646360707436015 159 0.996065492043722 0.00786901591255575 0.00393450795627788 160 0.997288066962462 0.0054238660750754 0.0027119330375377 161 0.998215783224078 0.00356843355184485 0.00178421677592242 162 0.998790310669573 0.00241937866085384 0.00120968933042692 163 0.99917136676269 0.00165726647462003 0.000828633237310013 164 0.999424959468564 0.00115008106287277 0.000575040531436386 165 0.999717760900023 0.000564478199954941 0.000282239099977471 166 0.999850004450857 0.000299991098285205 0.000149995549142603 167 0.999861742320295 0.000276515359409347 0.000138257679704673 168 0.999884120898819 0.000231758202363054 0.000115879101181527 169 0.99991662347431 0.000166753051378859 8.33765256894294e-05 170 0.999931289795268 0.000137420409464162 6.87102047320811e-05 171 0.9999547536797 9.04926405987435e-05 4.52463202993718e-05 172 0.999961609477624 7.67810447521787e-05 3.83905223760893e-05 173 0.999967060311952 6.58793760951498e-05 3.29396880475749e-05 174 0.999972322964217 5.53540715665964e-05 2.76770357832982e-05 175 0.999971304481096 5.73910378088878e-05 2.86955189044439e-05 176 0.999991596946086 1.68061078283379e-05 8.40305391416894e-06 177 0.999996333437447 7.33312510608517e-06 3.66656255304259e-06 178 0.999996341595434 7.31680913189805e-06 3.65840456594902e-06 179 0.99999642936528 7.1412694383795e-06 3.57063471918975e-06 180 0.999996641200543 6.71759891433285e-06 3.35879945716643e-06 181 0.999996783660063 6.43267987441769e-06 3.21633993720884e-06 182 0.999996123201846 7.75359630769475e-06 3.87679815384738e-06 183 0.999995852065133 8.29586973457165e-06 4.14793486728583e-06 184 0.999995513632637 8.97273472554589e-06 4.48636736277294e-06 185 0.999996361713877 7.27657224529743e-06 3.63828612264872e-06 186 0.999997291359494 5.41728101268793e-06 2.70864050634397e-06 187 0.999998240462096 3.51907580882338e-06 1.75953790441169e-06 188 0.999999074481425 1.85103714956888e-06 9.2551857478444e-07 189 0.999998991350882 2.01729823541480e-06 1.00864911770740e-06 190 0.999998983965927 2.03206814517489e-06 1.01603407258745e-06 191 0.999999447199513 1.10560097395871e-06 5.52800486979353e-07 192 0.999999815894924 3.68210152684674e-07 1.84105076342337e-07 193 0.999999880885959 2.38228081917909e-07 1.19114040958955e-07 194 0.999999892999771 2.1400045733368e-07 1.0700022866684e-07 195 0.999999882175344 2.35649312209088e-07 1.17824656104544e-07 196 0.999999836558858 3.26882283468851e-07 1.63441141734425e-07 197 0.999999827228833 3.45542333735279e-07 1.72771166867639e-07 198 0.999999754288713 4.91422574144486e-07 2.45711287072243e-07 199 0.99999981801767 3.63964658471047e-07 1.81982329235524e-07 200 0.999999979999293 4.00014141644473e-08 2.00007070822237e-08 201 0.999999963592392 7.28152166409564e-08 3.64076083204782e-08 202 0.99999995441973 9.11605403006793e-08 4.55802701503396e-08 203 0.99999993827403 1.23451940629548e-07 6.17259703147739e-08 204 0.999999943859982 1.12280036702505e-07 5.61400183512523e-08 205 0.999999964155906 7.16881871246911e-08 3.58440935623456e-08 206 0.999999933286518 1.33426964389076e-07 6.67134821945382e-08 207 0.99999989410576 2.11788478716174e-07 1.05894239358087e-07 208 0.999999826780934 3.46438132244349e-07 1.73219066122174e-07 209 0.99999970771275 5.84574497440654e-07 2.92287248720327e-07 210 0.999999605922306 7.88155387042195e-07 3.94077693521098e-07 211 0.999999788893587 4.22212826485131e-07 2.11106413242565e-07 212 0.999999999984963 3.00743388769278e-11 1.50371694384639e-11 213 0.999999999979928 4.01433519881858e-11 2.00716759940929e-11 214 0.999999999971417 5.71656762542014e-11 2.85828381271007e-11 215 0.999999999928264 1.43471084295844e-10 7.17355421479222e-11 216 0.999999999758127 4.83746122624029e-10 2.41873061312014e-10 217 0.999999998986928 2.02614389629136e-09 1.01307194814568e-09 218 0.99999999784709 4.30581927364998e-09 2.15290963682499e-09 219 0.999999996601666 6.79666784123918e-09 3.39833392061959e-09 220 0.999999988384818 2.32303639944522e-08 1.16151819972261e-08 221 0.999999997971118 4.05776438970551e-09 2.02888219485276e-09 222 0.99999999337198 1.32560400067936e-08 6.62802000339682e-09 223 0.999999980505623 3.89887545518536e-08 1.94943772759268e-08 224 0.99999991737412 1.65251760739019e-07 8.26258803695093e-08 225 0.999999971579537 5.68409250898239e-08 2.84204625449119e-08 226 0.999999858411454 2.83177092048119e-07 1.41588546024059e-07 227 0.999999649924018 7.00151964441769e-07 3.50075982220884e-07 228 0.999999642037195 7.15925610367283e-07 3.57962805183642e-07 229 0.999999652562062 6.9487587535896e-07 3.4743793767948e-07 230 0.99999742878135 5.14243729868059e-06 2.57121864934030e-06 231 0.999978055148042 4.38897039151055e-05 2.19448519575527e-05 232 0.999977352655018 4.52946899630782e-05 2.26473449815391e-05 233 0.99989749135034 0.000205017299321636 0.000102508649660818 234 0.999057828628617 0.00188434274276646 0.000942171371383229 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 170 0.776255707762557 NOK 5% type I error level 193 0.881278538812785 NOK 10% type I error level 199 0.908675799086758 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/106lta1229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/106lta1229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/1vu001229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/1vu001229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/2uj271229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/2uj271229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/3st411229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/3st411229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/40pmo1229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/40pmo1229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/5nwsw1229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/5nwsw1229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/6ogm41229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/6ogm41229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/71i4u1229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/71i4u1229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/8e0071229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/8e0071229074522.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/9prk41229074522.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229074658mup7j8gq4ncsg5x/9prk41229074522.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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