Viskosität

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 15 Feb 2008 19:30:37 -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/Feb/16/t12031291166k6mlw0pdt2b6mv.htm/, Retrieved Sat, 16 Feb 2008 03:32:10 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

2 990 40 760.00 2 990 30 389.00 2 990 20 201.00 2 2100 50 987.00 2 2100 40 517.00 2 2100 30 272.00 2 2100 20 144.00 5 500 20 141.00 5 500 30 266.00 5 500 40 504.00 5 500 50 960.00 5 2100 20 72.30 5 2100 30 128.00 5 2100 40 229.00 5 2100 50 413.00 5 2100 60 748.00 7 120 40 795.00 7 120 20 209.00 7 990 60 833.00 7 990 40 252.00 7 990 30 140.00 7 990 20 78.40 10 200 20 128.00 10 200 30 236.00 10 200 40 466.00 10 200 50 844.00 10 500 20 84.20 10 500 40 274.00 10 500 50 501.00 10 500 60 918.00 10 985 20 61.50 10 985 40 189.00 10 985 60 601.00 10 985 70 1076.00 10 990 20 60.00 10 990 40 184.00 10 990 60 580.00 10 990 70 1038.00 10 2100 20 42.90 10 2100 40 124.00 10 2100 60 369.00 10 2100 70 640.00 20 990 80 841.00 20 990 60 287.00 20 990 40 99.70 20 990 20 35.70 40 200 60 400.00 40 200 40 133.00 40 200 20 45.60 50 110 20 50.50 50 110 40 150.00 50 110 50 262.00 50 110 60 460.00 50 110 70 810.00 50 120 20 48.60 50 120 40 143.00 50 120 50 249.00 50 120 60 436.00 50 120 70 766.00 50 500 20 25.40 50 500 40 66.70 50 500 50 109.00 50 500 70 299.00 50 500 90 830.00 50 985 20 18.50 50 985 40 45.80 50 985 60 117.00 50 985 80 304.00 50 985 100 801.00 50 990 20 18.10 50 990 40 44.50 50 990 60 113.00 50 990 80 292.00 50 990 100 766.00 50 2100 20 12.90 50 2100 40 29.90 50 2100 70 111.00 50 2100 90 272.00 50 2100 120 1061.00 75 200 90 1007.00 75 200 75 461.00 75 200 60 212.00 75 200 20 28.60 100 105 20 30.90 100 105 40 84.10 100 105 60 236.00 100 105 80 673.00 100 985 20 11.10 100 985 40 25.00 100 985 60 58.70 100 985 80 137.00 100 985 100 327.00 100 985 120 790.00 100 990 20 10.80 100 990 40 24.20 100 990 65 69.00 100 990 90 202.00 100 990 120 752.00 100 2100 20 7.81 100 2100 40 16.50 100 2100 70 52.90 100 2100 90 118.00 100 2100 120 399.00 110 500 110 798.00 110 500 90 317.00 110 500 60 80.90 110 500 40 33.40 110 500 30 21.70 110 500 20 14.20 150 200 90 435.00 150 200 60 105.00 150 200 40 41.90 150 200 20 17.20 180 2100 20 5.17 180 2100 40 10.10 180 2100 60 20.30 180 2100 80 41.30 180 2100 100 85.40 180 2100 120 179.00 250 105 20 15.80 250 105 40 37.80 250 105 60 93.40 250 105 80 236.00 250 105 100 603.00 250 120 20 14.80 250 120 40 35.20 250 120 60 86.00 250 120 80 215.00 250 120 100 544.00 250 985 40 11.40 250 985 60 23.20 250 985 80 48.20 250 985 100 102.00 250 985 120 217.00 300 200 20 10.30 300 200 40 22.90 300 200 60 52.10 300 200 80 121.00 300 200 100 287.00 300 200 120 683.00 300 500 25 8.11 300 500 40 14.00 300 500 50 20.20 300 500 70 43.00 300 500 100 138.00 300 500 120 304.00 300 990 20 4.89 300 990 40 9.51 300 990 60 16.80 300 990 90 54.20 300 990 120 161.00 500 20 20 19.70 500 20 40 49.23 500 20 60 127.00 500 20 80 334.00 500 20 100 890.00 500 46 20 13.70 500 46 40 31.90 500 46 60 76.80 500 46 80 189.00 500 46 100 471.00 500 105 20 9.46 500 105 40 20.70 500 105 60 46.30 500 105 80 106.00 500 105 100 247.00 500 105 120 579.00 500 120 40 19.20 500 120 60 42.70 500 120 80 96.70 500 120 100 222.00 500 120 120 515.00 500 200 20 7.07 500 200 40 14.70 500 200 60 31.10 500 200 80 67.40 500 200 100 148.00 500 200 120 329.00 500 985 40 3.51 500 985 60 6.46 500 985 80 22.80 500 985 100 43.60 500 985 120 84.50 670 990 120 55.90 670 500 95 25.80 670 990 70 12.10 670 990 40 5.03 670 990 20 2.83 700 500 20 3.68 700 500 40 6.83 700 500 80 24.50 700 500 100 47.40 700 500 120 92.80 700 985 20 2.80 700 985 40 4.92 700 985 60 8.92 700 985 80 16.20 700 985 100 29.70 700 985 120 55.00 1000 20 20 11.90 1000 20 40 27.00 1000 20 60 63.20 1000 20 80 151.00 1000 20 100 367.00 1000 20 120 896.00 1000 46 40 17.50 1000 46 60 38.20 1000 46 80 85.00 1000 46 100 193.00 1000 46 120 440.00 1000 105 20 5.68 1000 105 40 11.30 1000 105 60 23.10 1000 105 80 47.90 1000 105 100 101.00 1000 105 120 215.00 1000 120 20 5.35 1000 120 40 10.60 1000 120 60 21.30 1000 120 80 43.60 1000 120 100 90.80 1000 120 120 191.00 1000 200 20 4.26 1000 200 40 8.08 1000 200 60 15.60 1000 200 80 30.60 1000 200 100 61.00 1000 200 120 123.00 1500 46 20 6.92 1500 46 40 14.30 1500 46 60 30.20 1500 46 80 65.10 1500 46 100 143.00 1500 46 120 315.00 1500 500 20 2.58 1500 500 40 4.51 1500 500 60 7.97 1500 500 90 19.10 1500 500 120 46.80 1700 500 120 44.50 1700 500 95 21.20 1700 500 70 10.20 1700 500 40 4.37 1700 500 20 2.51 2000 46 20 6.14 2000 46 40 12.40 2000 46 60 25.60 2000 46 80 54.00 2000 46 100 115.00 2000 46 120 250.00 2000 105 20 4.31 2000 105 40 8.20 2000 105 50 11.40 2000 105 70 22.20 2000 105 90 44.00 2000 200 20 3.31 2000 200 40 6.04 2000 200 60 11.10 2000 200 80 20.80 2000 200 100 39.50 2000 200 120 75.70 2500 20 20 8.00 2500 20 40 17.00 2500 20 60 36.80 2500 20 80 81.70 2500 20 100 184.00 2500 20 120 418.00 2500 120 20 3.75 2500 120 40 6.98 2500 120 60 13.20 2500 120 80 25.20 2500 120 100 49.00 2500 120 120 96.10 3000 46 120 180.00 3000 46 100 86.30 3000 46 80 41.70 3000 46 60 20.40 3000 46 40 10.20 3000 46 20 5.19 3500 200 120 54.80 3500 200 100 29.60 3500 200 80 16.10 3500 200 60 8.90 3500 200 40 4.97 3500 200 20 2.80 5000 20 20 5.97 5000 20 40 12.00 5000 20 70 35.60 5000 20 90 75.30 5000 20 120 236.00 5000 46 20 4.24 5000 46 40 8.05 5000 46 60 15.50 5000 46 80 30.40 5000 46 100 60.60 5000 46 120 122.00 5000 105 20 3.11 5000 105 40 5.62 5000 105 60 10.30 5000 105 80 19.00 5000 105 100 35.50 5000 105 120 67.20 5000 120 20 2.98 5000 120 40 5.34 5000 120 60 9.67 5000 120 80 17.70 5000 120 100 32.90 5000 120 120 61.80 10000 20 20 4.50 10000 20 40 8.62 10000 20 70 23.60 10000 20 90 47.20 10000 20 120 137.00 10000 46 20 3.30 10000 46 40 6.00 10000 46 60 11.10 10000 46 80 20.70 10000 46 100 39.20 10000 46 120 75.00 20000 20 120 82.40 20000 20 100 42.70 20000 20 80 22.30 20000 20 60 11.80 20000 20 40 6.37 20000 20 20 3.46 35000 20 120 58.50 35000 20 100 31.30 35000 20 80 17.00 35000 20 60 9.30 35000 20 40 5.16 35000 20 20 2.90 40 200 70 698.00 40 200 50 230.00

Text written by user:

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 compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 12 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Ergebnis [t] = + 0.420818243142633 -0.00854984625152386U[t] + 0.0560030435963667nD[t] + 2.4335602121075T[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) 0.420818243142633 29.94 0.0141 0.988794 0.494397 U -0.00854984625152386 0.002306 -3.7071 0.000246 0.000123 nD 0.0560030435963667 0.020975 2.67 0.007961 0.00398 T 2.4335602121075 0.376127 6.47 0 0 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 760 153.189140195334 606.810859804666 2 389 128.853538074268 260.146461925732 3 201 104.517935953192 96.4820640468077 4 987 239.688120708387 747.311879291613 5 517 215.352518587309 301.647481412691 6 272 191.016916466234 80.9830835337659 7 144 166.681314345159 -22.6813143451590 8 141 77.0507950522177 63.9492049477823 9 266 101.386397173293 164.613602826707 10 504 125.721999294368 378.278000705632 11 960 150.057601415443 809.942398584557 12 72.3 166.655664806405 -94.3556648064045 13 128 190.991266927480 -62.9912669274796 14 229 215.326869048555 13.6731309514454 15 413 239.662471169630 173.337528830370 16 748 263.998073290705 484.001926709295 17 795 104.423743035246 690.576256964754 18 209 55.7525387930953 153.247461206905 19 833 201.817595206235 631.182404793765 20 252 153.146390964084 98.8536090359155 21 140 128.810788843009 11.1892111569906 22 78.4 104.475186721934 -26.0751867219344 23 128 60.2071327420501 67.7928672579499 24 236 84.5427348631251 151.457265136875 25 466 108.878336984200 357.1216630158 26 844 133.213939105275 710.786060894725 27 84.2 77.0080458209601 7.19195417903991 28 274 125.679250063110 148.320749936890 29 501 150.014852184185 350.985147815815 30 918 174.350454305260 743.64954569474 31 61.5 104.169521965198 -42.6695219651980 32 189 152.840726207348 36.1592737926519 33 601 201.511930449498 399.488069550502 34 1076 225.847532570573 850.152467429427 35 60 104.449537183180 -44.4495371831798 36 184 153.12074142533 30.8792585746701 37 580 201.79194566748 378.20805433252 38 1038 226.127547788555 811.872452211445 39 42.9 166.612915575147 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180.451384814497 -155.251384814497 272 49 229.122589056647 -180.122589056647 273 96.1 277.793793298797 -181.693793298797 274 180 269.374644946904 -89.3746449469038 275 86.3 220.703440704754 -134.403440704754 276 41.7 172.032236462604 -130.332236462604 277 20.4 123.361032220453 -102.961032220453 278 10.2 74.6898279783034 -64.4898279783034 279 5.19 26.0186237361533 -20.8286237361533 280 54.8 273.724190534982 -218.924190534982 281 29.6 225.052986292832 -195.452986292832 282 16.1 176.381782050682 -160.281782050682 283 8.9 127.710577808532 -118.810577808532 284 4.97 79.0393735663819 -74.0693735663819 285 2.8 30.3681693242318 -27.5681693242318 286 5.97 7.46285209959996 -1.49285209959996 287 12 56.1340563417501 -44.1340563417501 288 35.6 129.140862704975 -93.5408627049752 289 75.3 177.812066947125 -102.512066947125 290 236 250.818873310351 -14.8188733103505 291 4.24 8.91893123310547 -4.67893123310547 292 8.05 57.5901354752556 -49.5401354752556 293 15.5 106.261339717406 -90.7613397174057 294 30.4 154.932543959556 -124.532543959556 295 60.6 203.603748201706 -143.003748201706 296 122 252.274952443856 -130.274952443856 297 3.11 12.2231108052911 -9.11311080529114 298 5.62 60.8943150474412 -55.2743150474412 299 10.3 109.565519289591 -99.2655192895913 300 19 158.236723531741 -139.236723531741 301 35.5 206.907927773892 -171.407927773892 302 67.2 255.579132016042 -188.379132016042 303 2.98 13.0631564592367 -10.0831564592367 304 5.34 61.7343607013867 -56.3943607013868 305 9.67 110.405564943537 -100.735564943537 306 17.7 159.076769185687 -141.376769185687 307 32.9 207.747973427837 -174.847973427837 308 61.8 256.419177669987 -194.619177669987 309 4.5 -35.2863791580194 39.7863791580194 310 8.62 13.3848250841307 -4.76482508413074 311 23.6 86.391631447356 -62.7916314473559 312 47.2 135.062835689506 -87.862835689506 313 137 208.069642052731 -71.0696420527312 314 3.3 -33.8303000245139 37.1303000245139 315 6 14.8409042176363 -8.84090421763626 316 11.1 63.5121084597864 -52.4121084597864 317 20.7 112.183312701937 -91.4833127019365 318 39.2 160.854516944087 -121.654516944087 319 75 209.525721186237 -134.525721186237 320 82.4 122.571179537492 -40.1711795374925 321 42.7 73.8999752953424 -31.1999752953424 322 22.3 25.2287710531923 -2.92877105319228 323 11.8 -23.4424331889578 35.2424331889578 324 6.37 -72.113637431108 78.483637431108 325 3.46 -120.784841673258 124.244841673258 326 58.5 -5.67651423536552 64.1765142353655 327 31.3 -54.3477184775156 85.6477184775156 328 17 -103.018922719666 120.018922719666 329 9.3 -151.690126961816 160.990126961816 330 5.16 -200.361331203966 205.521331203966 331 2.9 -249.032535446116 251.932535446116 332 698 181.628647959880 516.37135204012 333 230 132.957443717730 97.0425562822705 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.140867632073434 0.281735264146868 0.859132367926566 8 0.0560881295549597 0.112176259109919 0.94391187044504 9 0.0368516218465215 0.073703243693043 0.963148378153478 10 0.0195322190697717 0.0390644381395435 0.980467780930228 11 0.0155612236085501 0.0311224472171001 0.98443877639145 12 0.0103313394067961 0.0206626788135921 0.989668660593204 13 0.0080610268021526 0.0161220536043052 0.991938973197847 14 0.0178058433162204 0.0356116866324409 0.98219415668378 15 0.0258145019319016 0.0516290038638032 0.974185498068098 16 0.0160509596684932 0.0321019193369864 0.983949040331507 17 0.0474445133436246 0.0948890266872491 0.952555486656375 18 0.0320105962353133 0.0640211924706265 0.967989403764687 19 0.023589594068346 0.047179188136692 0.976410405931654 20 0.0242659529601442 0.0485319059202884 0.975734047039856 21 0.0152096021808623 0.0304192043617246 0.984790397819138 22 0.0113919818128870 0.0227839636257740 0.988608018187113 23 0.0108602417794445 0.0217204835588891 0.989139758220555 24 0.0066238103122948 0.0132476206245896 0.993376189687705 25 0.00429826862342486 0.00859653724684972 0.995701731376575 26 0.006753404061348 0.013506808122696 0.993246595938652 27 0.00485860080210549 0.00971720160421097 0.995141399197895 28 0.00444236257185557 0.00888472514371114 0.995557637428144 29 0.00370433693375145 0.00740867386750289 0.996295663066249 30 0.00414701670588357 0.00829403341176715 0.995852983294116 31 0.00402167215073166 0.00804334430146331 0.995978327849268 32 0.00363683824536267 0.00727367649072534 0.996363161754637 33 0.00347527821082996 0.00695055642165991 0.99652472178917 34 0.0062593892940404 0.0125187785880808 0.99374061070596 35 0.00587362084653193 0.0117472416930639 0.994126379153468 36 0.00551400095916075 0.0110280019183215 0.99448599904084 37 0.00598665619533291 0.0119733123906658 0.994013343804667 38 0.0100168218884814 0.0200336437769627 0.989983178111519 39 0.0210145822697851 0.0420291645395703 0.978985417730215 40 0.015889283509543 0.031778567019086 0.984110716490457 41 0.0187639213951594 0.0375278427903187 0.98123607860484 42 0.0184433275114528 0.0368866550229056 0.981556672488547 43 0.0263148547211081 0.0526297094422161 0.973685145278892 44 0.0205500948656373 0.0411001897312747 0.979449905134363 45 0.0189483675046723 0.0378967350093445 0.981051632495328 46 0.061250588684347 0.122501177368694 0.938749411315653 47 0.114759755518800 0.229519511037600 0.8852402444812 48 0.138784721734743 0.277569443469486 0.861215278265257 49 0.245263268149824 0.490526536299648 0.754736731850176 50 0.364028730084073 0.728057460168147 0.635971269915927 51 0.330418104809074 0.660836209618149 0.669581895190926 52 0.296123447500545 0.592246895001091 0.703876552499455 53 0.282426166428039 0.564852332856078 0.71757383357196 54 0.394888391952735 0.78977678390547 0.605111608047265 55 0.411909455891484 0.823818911782969 0.588090544108516 56 0.372076109257337 0.744152218514674 0.627923890742663 57 0.341807377408013 0.683614754816026 0.658192622591987 58 0.325653325047207 0.651306650094413 0.674346674952793 59 0.404445679673068 0.808891359346136 0.595554320326932 60 0.417521095939438 0.835042191878876 0.582478904060562 61 0.382872049033365 0.76574409806673 0.617127950966635 62 0.378159071021071 0.756318142042142 0.621840928978929 63 0.428551037317994 0.857102074635989 0.571448962682006 64 0.498194204684212 0.996388409368425 0.501805795315788 65 0.536817407710262 0.926365184579477 0.463182592289738 66 0.497448981948281 0.994897963896562 0.502551018051719 67 0.522115510619012 0.955768978761976 0.477884489380988 68 0.59576473599054 0.80847052801892 0.40423526400946 69 0.66081900451584 0.67836199096832 0.33918099548416 70 0.687936967313747 0.624126065372506 0.312063032686253 71 0.652975589086305 0.694048821827389 0.347024410913695 72 0.660356125445734 0.679287749108531 0.339643874554266 73 0.705254441782502 0.589491116434996 0.294745558217498 74 0.760728290037789 0.478543419924423 0.239271709962211 75 0.82648823152811 0.347023536943781 0.173511768471891 76 0.80591028417294 0.388179431654119 0.194089715827059 77 0.810551289077222 0.378897421845557 0.189448710922778 78 0.834811115189887 0.330377769620225 0.165188884810113 79 0.947669390540996 0.104661218918008 0.0523306094590042 80 0.997265869184826 0.00546826163034776 0.00273413081517388 81 0.997643882316088 0.00471223536782441 0.00235611768391221 82 0.997052200730425 0.00589559853914966 0.00294779926957483 83 0.998070206571627 0.00385958685674685 0.00192979342837342 84 0.9993697971509 0.00126040569820118 0.00063020284910059 85 0.999367548166584 0.00126490366683206 0.000632451833416032 86 0.999268550144334 0.00146289971133119 0.000731449855665594 87 0.9998601518481 0.00027969630380143 0.000139848151900715 88 0.999943157987464 0.000113684025072675 5.68420125363377e-05 89 0.999930999872197 0.000138000255606017 6.90001278030087e-05 90 0.999905465891141 0.000189068217717003 9.45341088585016e-05 91 0.9999016254385 0.000196749122999897 9.83745614999486e-05 92 0.999913782098038 0.000172435803924426 8.62179019622128e-05 93 0.999983550952237 3.28980955269440e-05 1.64490477634720e-05 94 0.999991592670564 1.68146588719529e-05 8.40732943597644e-06 95 0.999989196986977 2.16060260460195e-05 1.08030130230097e-05 96 0.99998564745026 2.87050994778571e-05 1.43525497389285e-05 97 0.999986724060139 2.65518797223599e-05 1.32759398611799e-05 98 0.999997857330973 4.28533805434254e-06 2.14266902717127e-06 99 0.999999350873644 1.29825271180884e-06 6.49126355904422e-07 100 0.999999281860658 1.43627868487984e-06 7.1813934243992e-07 101 0.999999007381438 1.98523712332900e-06 9.92618561664501e-07 102 0.999999034271872 1.93145625672125e-06 9.65728128360627e-07 103 0.999999488418839 1.02316232187739e-06 5.11581160938697e-07 104 0.999999985745253 2.85094950228605e-08 1.42547475114302e-08 105 0.999999985719003 2.85619945996094e-08 1.42809972998047e-08 106 0.99999997766191 4.46761807308948e-08 2.23380903654474e-08 107 0.999999968747061 6.25058772846464e-08 3.12529386423232e-08 108 0.999999965217891 6.95642171123254e-08 3.47821085561627e-08 109 0.999999971982791 5.60344174541957e-08 2.80172087270978e-08 110 0.999999985842504 2.83149910690596e-08 1.41574955345298e-08 111 0.99999998000998 3.99800384463975e-08 1.99900192231988e-08 112 0.999999980075905 3.98481902552358e-08 1.99240951276179e-08 113 0.99999999037096 1.92580803537502e-08 9.62904017687508e-09 114 0.999999999477508 1.04498370578102e-09 5.2249185289051e-10 115 0.99999999974151 5.16980427789416e-10 2.58490213894708e-10 116 0.999999999681867 6.362656167331e-10 3.1813280836655e-10 117 0.999999999512206 9.75588007348257e-10 4.87794003674128e-10 118 0.999999999368386 1.26322851698399e-09 6.31614258491993e-10 119 0.999999999436183 1.12763332521922e-09 5.63816662609612e-10 120 0.999999999924465 1.51069976202865e-10 7.55349881014326e-11 121 0.999999999949996 1.00007888902026e-10 5.00039444510128e-11 122 0.99999999993903 1.21942028836554e-10 6.09710144182768e-11 123 0.999999999927575 1.44849052670328e-10 7.2424526335164e-11 124 0.999999999996531 6.9372169029214e-12 3.4686084514607e-12 125 0.999999999997978 4.04494734865663e-12 2.02247367432832e-12 126 0.999999999997442 5.11546309460839e-12 2.55773154730420e-12 127 0.999999999995786 8.42723972767653e-12 4.21361986383827e-12 128 0.999999999993533 1.29344660912066e-11 6.4672330456033e-12 129 0.999999999999104 1.79240835605501e-12 8.96204178027507e-13 130 0.999999999998987 2.02554482074901e-12 1.01277241037451e-12 131 0.9999999999983 3.40128122170279e-12 1.70064061085140e-12 132 0.999999999997114 5.77235455301448e-12 2.88617727650724e-12 133 0.999999999995868 8.26387672218158e-12 4.13193836109079e-12 134 0.999999999995174 9.65257395293575e-12 4.82628697646788e-12 135 0.999999999997443 5.11469818937516e-12 2.55734909468758e-12 136 0.999999999997014 5.971191815716e-12 2.985595907858e-12 137 0.999999999995247 9.50645814736324e-12 4.75322907368162e-12 138 0.999999999991807 1.63865705861370e-11 8.19328529306851e-12 139 0.999999999989038 2.19234198418195e-11 1.09617099209097e-11 140 0.999999999999799 4.02430839357596e-13 2.01215419678798e-13 141 0.999999999999835 3.29807740923564e-13 1.64903870461782e-13 142 0.999999999999778 4.43241929261823e-13 2.21620964630911e-13 143 0.999999999999644 7.11278803639704e-13 3.55639401819852e-13 144 0.999999999999371 1.25768941325726e-12 6.28844706628631e-13 145 0.999999999998997 2.00536445425573e-12 1.00268222712787e-12 146 0.999999999998826 2.34806311906948e-12 1.17403155953474e-12 147 0.999999999999303 1.39449512868691e-12 6.97247564343455e-13 148 0.999999999999157 1.68655462801720e-12 8.43277314008598e-13 149 0.999999999998595 2.81072887258477e-12 1.40536443629238e-12 150 0.999999999997697 4.60585495312819e-12 2.30292747656409e-12 151 0.99999999999705 5.89986656592061e-12 2.94993328296031e-12 152 0.999999999999438 1.12439515608719e-12 5.62197578043595e-13 153 0.999999999999675 6.5029877239106e-13 3.2514938619553e-13 154 0.999999999999717 5.65883071527794e-13 2.82941535763897e-13 155 0.999999999999906 1.88504732901967e-13 9.42523664509835e-14 156 1 3.284577116091e-20 1.6422885580455e-20 157 1 2.89377522744895e-20 1.44688761372448e-20 158 1 4.33035861319088e-20 2.16517930659544e-20 159 1 8.1029357198793e-20 4.05146785993965e-20 160 1 1.36403441935019e-19 6.82017209675095e-20 161 1 9.82152823641726e-21 4.91076411820863e-21 162 1 1.22520290788819e-20 6.12601453944094e-21 163 1 2.20782866681920e-20 1.10391433340960e-20 164 1 4.51848392018389e-20 2.25924196009194e-20 165 1 9.61779614855968e-20 4.80889807427984e-20 166 1 1.51279431498139e-19 7.56397157490696e-20 167 1 1.51695503929426e-21 7.5847751964713e-22 168 1 2.90749356979254e-21 1.45374678489627e-21 169 1 6.17184438166167e-21 3.08592219083084e-21 170 1 1.33881008765489e-20 6.69405043827446e-21 171 1 2.29466140866686e-20 1.14733070433343e-20 172 1 6.89015910497443e-22 3.44507955248722e-22 173 1 1.03686170533783e-21 5.18430852668913e-22 174 1 2.08527299679481e-21 1.04263649839740e-21 175 1 4.49380245847972e-21 2.24690122923986e-21 176 1 9.47650306234823e-21 4.73825153117411e-21 177 1 1.91474890874446e-20 9.5737445437223e-21 178 1 1.41696079961184e-20 7.0848039980592e-21 179 1 2.33056393368334e-20 1.16528196684167e-20 180 1 4.89071399396622e-20 2.44535699698311e-20 181 1 1.05118805458977e-19 5.25594027294883e-20 182 1 2.04572163507370e-19 1.02286081753685e-19 183 1 3.45356832750871e-19 1.72678416375436e-19 184 1 7.12882035076447e-19 3.56441017538224e-19 185 1 1.35566023118132e-18 6.77830115590662e-19 186 1 2.38564167566047e-18 1.19282083783024e-18 187 1 2.49696304050258e-18 1.24848152025129e-18 188 1 1.57713030980336e-18 7.88565154901678e-19 189 1 1.50589732115091e-18 7.52948660575457e-19 190 1 2.30724061140686e-18 1.15362030570343e-18 191 1 4.71591642415161e-18 2.35795821207581e-18 192 1 9.3916886766166e-18 4.6958443383083e-18 193 1 1.83643173256608e-17 9.18215866283038e-18 194 1 1.38126392951440e-17 6.90631964757201e-18 195 1 1.68883523427366e-17 8.4441761713683e-18 196 1 2.77465494005123e-17 1.38732747002562e-17 197 1 5.28078864976018e-17 2.64039432488009e-17 198 1 1.03164244518721e-16 5.15821222593605e-17 199 1 1.87742208719482e-16 9.38711043597409e-17 200 1 1.34456809492741e-16 6.72284047463706e-17 201 1 1.52536397508122e-16 7.6268198754061e-17 202 1 2.20697562275985e-16 1.10348781137992e-16 203 1 3.27854906862849e-16 1.63927453431424e-16 204 1 8.38173433069442e-17 4.19086716534721e-17 205 1 5.72584923474069e-28 2.86292461737034e-28 206 1 1.15274145317021e-27 5.76370726585106e-28 207 1 2.69718274125e-27 1.348591370625e-27 208 1 7.01413467118744e-27 3.50706733559372e-27 209 1 1.29780862627549e-26 6.48904313137744e-27 210 1 7.98373336386829e-29 3.99186668193414e-29 211 1 1.52929213489331e-28 7.64646067446657e-29 212 1 3.63330641928631e-28 1.81665320964315e-28 213 1 9.30058455044838e-28 4.65029227522419e-28 214 1 2.47269946881983e-27 1.23634973440992e-27 215 1 6.99587843623438e-27 3.49793921811719e-27 216 1 1.19041497752371e-26 5.95207488761855e-27 217 1 2.45600702230275e-26 1.22800351115138e-26 218 1 5.96906084501984e-26 2.98453042250992e-26 219 1 1.51792471769677e-25 7.58962358848387e-26 220 1 3.91546998104052e-25 1.95773499052026e-25 221 1 1.06577702670845e-24 5.32888513354226e-25 222 1 2.20643088812109e-24 1.10321544406055e-24 223 1 4.65318879709110e-24 2.32659439854555e-24 224 1 1.13799000338543e-23 5.68995001692714e-24 225 1 2.87166121471276e-23 1.43583060735638e-23 226 1 7.08161812804792e-23 3.54080906402396e-23 227 1 1.75285207725708e-22 8.76426038628538e-23 228 1 4.49301953996623e-22 2.24650976998312e-22 229 1 5.87290888743549e-22 2.93645444371774e-22 230 1 1.02207955094486e-21 5.1103977547243e-22 231 1 2.07391311427415e-21 1.03695655713707e-21 232 1 4.74419334032995e-21 2.37209667016498e-21 233 1 1.05403308046653e-20 5.27016540233266e-21 234 1 2.98936411376013e-21 1.49468205688006e-21 235 1 4.70508842922013e-21 2.35254421461007e-21 236 1 9.72225648851222e-21 4.86112824425611e-21 237 1 2.32592735371971e-20 1.16296367685986e-20 238 1 5.8545845655599e-20 2.92729228277995e-20 239 1 1.42736513479789e-19 7.13682567398945e-20 240 1 3.35582145032893e-19 1.67791072516447e-19 241 1 7.52092411808614e-19 3.76046205904307e-19 242 1 1.62483987128376e-18 8.12419935641882e-19 243 1 3.20113941406587e-18 1.60056970703293e-18 244 1 5.65354034843447e-18 2.82677017421723e-18 245 1 9.17405752122393e-18 4.58702876061196e-18 246 1 1.76777698063132e-17 8.83888490315661e-18 247 1 3.70501723992511e-17 1.85250861996256e-17 248 1 8.31139112992132e-17 4.15569556496066e-17 249 1 1.93265344854607e-16 9.66326724273035e-17 250 1 1.90652496483119e-16 9.53262482415596e-17 251 1 3.64789218853683e-16 1.82394609426841e-16 252 1 7.68358779704356e-16 3.84179389852178e-16 253 1 1.65292433821235e-15 8.26462169106174e-16 254 0.999999999999998 3.5847230211092e-15 1.7923615105546e-15 255 0.999999999999996 7.90443543362432e-15 3.95221771681216e-15 256 0.999999999999992 1.58845289477109e-14 7.94226447385543e-15 257 0.999999999999983 3.38111845683869e-14 1.69055922841934e-14 258 0.999999999999964 7.12363842615179e-14 3.56181921307589e-14 259 0.999999999999929 1.43135821897578e-13 7.1567910948789e-14 260 0.999999999999861 2.77055226930601e-13 1.38527613465300e-13 261 0.99999999999972 5.58769313482435e-13 2.79384656741218e-13 262 0.999999999999497 1.00591160074102e-12 5.02955800370511e-13 263 0.999999999999005 1.99030052519111e-12 9.95150262595556e-13 264 0.999999999997929 4.14284823451621e-12 2.07142411725811e-12 265 0.999999999995579 8.84227597326776e-12 4.42113798663388e-12 266 0.99999999999336 1.32810826951140e-11 6.64054134755701e-12 267 0.999999999999876 2.47486923239015e-13 1.23743461619508e-13 268 0.999999999999742 5.16262984936018e-13 2.58131492468009e-13 269 0.999999999999448 1.10441201245723e-12 5.52206006228615e-13 270 0.999999999998832 2.33609483323242e-12 1.16804741661621e-12 271 0.999999999997564 4.87177071272421e-12 2.43588535636210e-12 272 0.999999999994787 1.04261312211441e-11 5.21306561057207e-12 273 0.999999999987873 2.42546261733232e-11 1.21273130866616e-11 274 0.9999999999808 3.83996589836929e-11 1.91998294918464e-11 275 0.99999999995667 8.66614454196166e-11 4.33307227098083e-11 276 0.999999999903675 1.92649326727174e-10 9.63246633635872e-11 277 0.999999999793714 4.12571946602828e-10 2.06285973301414e-10 278 0.999999999571895 8.56210032101094e-10 4.28105016050547e-10 279 0.999999999144305 1.71139065057904e-09 8.5569532528952e-10 280 0.999999998599207 2.80158630298594e-09 1.40079315149297e-09 281 0.99999999808546 3.82908233996975e-09 1.91454116998487e-09 282 0.999999997653498 4.69300463401848e-09 2.34650231700924e-09 283 0.999999997306011 5.38797776658152e-09 2.69398888329076e-09 284 0.999999997107663 5.78467454754136e-09 2.89233727377068e-09 285 0.999999997295747 5.40850659007342e-09 2.70425329503671e-09 286 0.999999995479594 9.04081242680053e-09 4.52040621340027e-09 287 0.999999991426334 1.71473312641353e-08 8.57366563206767e-09 288 0.99999998252407 3.49518599594873e-08 1.74759299797436e-08 289 0.999999966485363 6.70292735931329e-08 3.35146367965665e-08 290 0.999999987036177 2.59276464714262e-08 1.29638232357131e-08 291 0.999999973744473 5.25110548069329e-08 2.62555274034665e-08 292 0.999999943684907 1.12630186935557e-07 5.63150934677784e-08 293 0.999999876037995 2.47924010717954e-07 1.23962005358977e-07 294 0.999999725883407 5.48233185445528e-07 2.74116592722764e-07 295 0.999999408082147 1.18383570605857e-06 5.91917853029284e-07 296 0.999998954225275 2.09154944920624e-06 1.04577472460312e-06 297 0.999998116479742 3.76704051679349e-06 1.88352025839674e-06 298 0.999996778869197 6.4422616057082e-06 3.2211308028541e-06 299 0.999994768479139 1.04630417217798e-05 5.23152086088989e-06 300 0.999991796860936 1.64062781279658e-05 8.20313906398288e-06 301 0.999987157273028 2.5685453944563e-05 1.28427269722815e-05 302 0.999978610123006 4.27797539870382e-05 2.13898769935191e-05 303 0.999971941908254 5.61161834909839e-05 2.80580917454919e-05 304 0.999970369669884 5.92606602328317e-05 2.96303301164158e-05 305 0.99997550549293 4.89890141405716e-05 2.44945070702858e-05 306 0.99998488098013 3.02380397378067e-05 1.51190198689033e-05 307 0.999993915535681 1.21689286377028e-05 6.08446431885142e-06 308 0.999998970369787 2.05926042519704e-06 1.02963021259852e-06 309 0.999997843822813 4.31235437473888e-06 2.15617718736944e-06 310 0.99999498227529 1.00354494215834e-05 5.01772471079171e-06 311 0.999987388104719 2.52237905629019e-05 1.26118952814509e-05 312 0.99996903515805 6.19296839011552e-05 3.09648419505776e-05 313 0.999957932824203 8.4134351593899e-05 4.20671757969495e-05 314 0.999893601683167 0.000212796633665821 0.000106398316832910 315 0.999738319338809 0.000523361322382435 0.000261680661191217 316 0.999394438546368 0.00121112290726407 0.000605561453632033 317 0.998692259547224 0.0026154809055529 0.00130774045277645 318 0.997312621651107 0.00537475669778539 0.00268737834889269 319 0.994508128929995 0.0109837421400107 0.00549187107000533 320 0.988135164017387 0.0237296719652268 0.0118648359826134 321 0.975216454552603 0.0495670908947938 0.0247835454473969 322 0.950761056395564 0.0984778872088712 0.0492389436044356 323 0.906632837115516 0.186734325768967 0.0933671628844836 324 0.831656851027833 0.336686297944335 0.168343148972167 325 0.714627989168675 0.570744021662649 0.285372010831325 326 0.554188298169594 0.891623403660812 0.445811701830406 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 247 0.771875 NOK 5% type I error level 274 0.85625 NOK 10% type I error level 280 0.875 NOK

Charts produced by software:

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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