Met dummy variabele 9/11 aanslagen

*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: Sat, 13 Dec 2008 08:45:07 -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/13/t1229183164ollhd3n8uyul2al.htm/, Retrieved Sat, 13 Dec 2008 16:46:14 +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/2008/Dec/13/t1229183164ollhd3n8uyul2al.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

32,68 10967,87 0 31,54 10433,56 0 32,43 10665,78 0 26,54 10666,71 0 25,85 10682,74 0 27,6 10777,22 0 25,71 10052,6 0 25,38 10213,97 0 28,57 10546,82 0 27,64 10767,2 0 25,36 10444,5 0 25,9 10314,68 0 26,29 9042,56 1 21,74 9220,75 1 19,2 9721,84 1 19,32 9978,53 1 19,82 9923,81 1 20,36 9892,56 1 24,31 10500,98 1 25,97 10179,35 1 25,61 10080,48 1 24,67 9492,44 1 25,59 8616,49 1 26,09 8685,4 1 28,37 8160,67 1 27,34 8048,1 1 24,46 8641,21 1 27,46 8526,63 1 30,23 8474,21 1 32,33 7916,13 1 29,87 7977,64 1 24,87 8334,59 1 25,48 8623,36 1 27,28 9098,03 1 28,24 9154,34 1 29,58 9284,73 1 26,95 9492,49 1 29,08 9682,35 1 28,76 9762,12 1 29,59 10124,63 1 30,7 10540,05 1 30,52 10601,61 1 32,67 10323,73 1 33,19 10418,4 1 37,13 10092,96 1 35,54 10364,91 1 37,75 10152,09 1 41,84 10032,8 1 42,94 10204,59 1 49,14 10001,6 1 44,61 10411,75 1 40,22 10673,38 1 44,23 10539,51 1 45,85 10723,78 1 53,38 10682,06 1 53,26 10283,19 1 51,8 10377,18 1 55,3 10486,64 1 57,81 10545,38 1 63,96 10554,27 1 63,77 10532,54 1 59,15 10324,31 1 56,12 10695,25 1 57,42 10827,81 1 63,52 10872,48 1 61,71 10971,19 1 63,01 11145,65 1 68,18 11234,68 1 72,03 11333,88 1 69,75 10997,97 1 74,41 11036,89 1 74,33 11257,35 1 64,24 11533,59 1 60,03 11963,12 1 59,44 12185,15 1 62,5 12377,62 1 55,04 12512,89 1 58,34 12631,48 1 61,92 12268,53 1 67,65 12754,8 1 67,68 13407,75 1 70,3 13480,21 1 75,26 13673,28 1 71,44 13239,71 1 76,36 13557,69 1 81,71 13901,28 1 92,6 13200,58 1 90,6 13406,97 1 92,23 12538,12 1 94,09 12419,57 1 102,79 12193,88 1 109,65 12656,63 1 124,05 12812,48 1 132,69 12056,67 1 135,81 11322,38 1 116,07 11530,75 1 101,42 11114,08 1 75,73 9181,73 1 55,48 8614,55 1

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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Olieprijs[t] = + 11.9170891434106 + 0.00113014834823295DowJones[t] -20.9453283476568`Dummy(9/11)`[t] + 0.382292664115397M1[t] -3.38414998743818M2[t] -6.94836441305219M3[t] -4.69119072629883M4[t] -4.5609608844506M5[t] -4.32867074945922M6[t] -2.29622302998753M7[t] -1.55286734952785M8[t] + 0.366270997172950M9[t] + 0.85732370177522M10[t] + 2.30748796734719M11[t] + 0.936438289044728t + 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) 11.9170891434106 14.185746 0.8401 0.403251 0.201626 DowJones 0.00113014834823295 0.001318 0.8572 0.393781 0.196891 `Dummy(9/11)` -20.9453283476568 5.199131 -4.0286 0.000123 6.1e-05 M1 0.382292664115397 5.688499 0.0672 0.946579 0.473289 M2 -3.38414998743818 5.683693 -0.5954 0.553168 0.276584 M3 -6.94836441305219 5.680549 -1.2232 0.22468 0.11234 M4 -4.69119072629883 5.904899 -0.7945 0.429168 0.214584 M5 -4.5609608844506 5.885013 -0.775 0.440508 0.220254 M6 -4.32867074945922 5.873888 -0.7369 0.463216 0.231608 M7 -2.29622302998753 5.857311 -0.392 0.696031 0.348016 M8 -1.55286734952785 5.861558 -0.2649 0.791717 0.395859 M9 0.366270997172950 5.871855 0.0624 0.95041 0.475205 M10 0.85732370177522 5.858196 0.1463 0.883999 0.441999 M11 2.30748796734719 5.842513 0.3949 0.693882 0.346941 t 0.936438289044728 0.078526 11.9253 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.918774071288138 R-squared 0.84414579407138 Adjusted R-squared 0.818170093083276 F-TEST (value) 32.4975173704837 F-TEST (DF numerator) 14 F-TEST (DF denominator) 84 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.6838049009502 Sum Squared Residuals 11466.9489449312 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 25.6311402607047 7.04885973929528 2 31.54 22.1972863342513 9.34271366574865 3 32.43 19.8319532471088 12.5980467528912 4 26.54 23.0266162608707 3.51338373912933 5 25.85 24.1114006697858 1.73859933021419 6 27.6 25.3869055097630 2.21309449023703 7 25.71 27.5368634221828 -1.82686342218283 8 25.38 29.3990294306416 -4.01902943064159 9 28.57 32.6307759440964 -4.06077594409645 10 27.64 34.3073290307270 -6.66732903072703 11 25.36 36.3292327133689 -10.9692327133689 12 25.9 34.8114671764989 -8.91146717649888 13 26.29 13.7471854652481 12.5428145347519 14 21.74 11.1185622369109 10.6214377630891 15 19.2 9.0570921361576 10.1429078638424 16 19.32 12.5408018914637 6.77919810853635 17 19.82 13.5456283047413 6.2743716952587 18 20.36 14.6790395928951 5.68096040710485 19 24.31 18.3355304594434 5.97446954055657 20 25.97 19.6518348157057 6.3181651842943 21 25.61 22.3956736842614 3.21432631573858 22 24.67 23.1585922432135 1.51140775678648 23 25.59 24.5552413521956 1.03475864780443 24 26.09 23.2620701965698 2.82792980343017 25 28.37 23.9877784069617 4.38222159303832 26 27.34 21.0305532448923 6.30944675510774 27 24.46 19.0730793951434 5.38692060485659 28 27.46 22.1371989732010 5.32280102679904 29 30.23 23.1446247276796 7.08537527232044 30 32.33 23.6826399615338 8.64736003846617 31 29.87 26.7210413949500 3.14895860504996 32 24.87 28.8042418173562 -3.93424181735621 33 25.48 31.986171391621 -6.50617139162097 34 27.28 33.9501099017237 -6.6701099017237 35 28.24 36.4003511098294 -8.1603511098294 36 29.58 35.176661474653 -5.59666147465302 37 26.95 36.7301920486420 -9.78019204864203 38 29.08 34.1147576515287 -5.03475765152869 39 28.76 31.5771334486979 -2.81713344869795 40 29.59 35.1804355022140 -5.59043550221396 41 30.7 36.7165898599299 -6.01658985992985 42 30.52 37.9548902162832 -7.43489021628319 43 32.67 40.6097306017926 -7.93973060179262 44 33.19 42.3965157154243 -9.20651571542426 45 37.13 44.8842968727209 -7.75429687272085 46 35.54 46.6191317096698 -11.0791317096698 47 37.75 48.7652160928156 -11.0152160928156 48 41.84 47.2593510180524 -5.41935101805237 49 42.94 48.7722301559554 -5.83223015595545 50 49.14 45.7128169802388 3.42718301976121 51 44.61 43.5485711886973 1.06142881130275 52 40.22 47.0378638768435 -6.81786387684352 53 44.23 47.9532390483585 -3.72323904835854 54 45.85 49.3302199085235 -3.48021990852354 55 53.38 52.2519561279517 1.12804387204833 56 53.26 53.4809678257964 -0.220967825796409 57 51.8 56.4427671047924 -4.64276710479236 58 55.3 57.9939641366369 -2.69396413663693 59 57.81 60.4469516052288 -2.63695160522883 60 63.96 59.0859489457422 4.87405105425785 61 63.77 60.3801217752952 3.38987822470482 62 59.15 57.3147866222338 1.83521337776622 63 56.12 55.106227713958 1.01377228604197 64 57.42 58.4496521547979 -1.02965215479787 65 63.52 59.5668040124064 3.9531959875936 66 61.71 60.8470893798966 0.862910620103401 67 63.01 64.0131410692457 -1.00314106924572 68 68.18 65.7935521461933 2.38644785380669 69 72.03 68.7612394980836 3.26876050191645 70 69.75 69.8091023600756 -0.059102360075612 71 74.41 72.2396902884056 2.17030971159445 72 74.33 71.1177931149545 3.21220688504548 73 64.24 72.7487162478305 -8.50871624783051 74 60.03 70.4041445053382 -10.3741445053382 75 59.44 68.027295206527 -8.58729520652704 76 62.5 71.4384268349095 -8.93842683490952 77 55.04 72.6579701328679 -17.6179701328680 78 58.34 73.960722849521 -15.6207228495210 79 61.92 76.5194215150463 -14.5994215150463 80 67.65 78.748772721846 -11.0987727218459 81 67.68 82.3422797215702 -14.6622797215701 82 70.3 83.8516612645301 -13.5516612645301 83 75.26 86.4564615607402 -11.1964615607401 84 71.44 84.5954134630943 -13.1554134630943 85 76.36 86.2735089880256 -9.91350898802556 86 81.71 83.831812296486 -2.12181229648608 87 92.6 80.41214121231 12.1878587876900 88 90.6 83.8390045056998 6.76099549430015 89 92.23 83.9237432442306 8.3062567557694 90 94.09 84.9584925815837 9.1315074184163 91 102.79 87.6723154093874 15.1176845906126 92 109.65 89.8750855270366 19.7749144729634 93 124.05 92.9067957828543 31.1432042171457 94 132.69 93.4801093534233 39.2098906465767 95 135.81 95.036855277416 40.773144722584 96 116.07 93.9012946104349 22.1687053895651 97 101.42 94.7491266513368 6.67087334866323 98 75.73 89.73528012812 -14.0052801281200 99 55.48 86.4665064514 -30.9865064514000 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0114276550477676 0.0228553100955351 0.988572344952232 19 0.00426692227186226 0.00853384454372451 0.995733077728138 20 0.00187557273905165 0.00375114547810329 0.998124427260948 21 0.000447633128175194 0.000895266256350388 0.999552366871825 22 0.000193198249654935 0.000386396499309869 0.999806801750345 23 0.000232782664776963 0.000465565329553926 0.999767217335223 24 0.000122239420895757 0.000244478841791513 0.999877760579104 25 3.1839030224961e-05 6.3678060449922e-05 0.999968160969775 26 8.74401954600158e-06 1.74880390920032e-05 0.999991255980454 27 2.38108182625823e-06 4.76216365251645e-06 0.999997618918174 28 1.18566223171301e-06 2.37132446342603e-06 0.999998814337768 29 1.03541328556437e-06 2.07082657112873e-06 0.999998964586714 30 8.26034213330657e-07 1.65206842666131e-06 0.999999173965787 31 2.3875244462465e-07 4.775048892493e-07 0.999999761247555 32 9.1410893672212e-08 1.82821787344424e-07 0.999999908589106 33 3.63434932830915e-08 7.26869865661831e-08 0.999999963656507 34 8.80726799078906e-09 1.76145359815781e-08 0.999999991192732 35 2.20921519828054e-09 4.41843039656109e-09 0.999999997790785 36 5.48702950925532e-10 1.09740590185106e-09 0.999999999451297 37 4.3610654574529e-10 8.7221309149058e-10 0.999999999563894 38 1.04623100666037e-10 2.09246201332074e-10 0.999999999895377 39 2.71752608236910e-11 5.43505216473821e-11 0.999999999972825 40 7.77176894438044e-12 1.55435378887609e-11 0.999999999992228 41 2.21454518330377e-12 4.42909036660754e-12 0.999999999997785 42 4.72685124263918e-13 9.45370248527837e-13 0.999999999999527 43 1.25811082516961e-13 2.51622165033921e-13 0.999999999999874 44 4.79367543190413e-14 9.58735086380826e-14 0.999999999999952 45 3.98948652170828e-14 7.97897304341656e-14 0.99999999999996 46 1.68623386924318e-14 3.37246773848636e-14 0.999999999999983 47 1.51962118454042e-14 3.03924236908084e-14 0.999999999999985 48 3.70824468770053e-14 7.41648937540105e-14 0.999999999999963 49 1.52487132347448e-14 3.04974264694896e-14 0.999999999999985 50 1.61478506034973e-13 3.22957012069946e-13 0.999999999999839 51 1.74952980036520e-13 3.49905960073039e-13 0.999999999999825 52 4.65956434504152e-14 9.31912869008304e-14 0.999999999999953 53 2.10664472161413e-14 4.21328944322826e-14 0.999999999999979 54 9.4496318999254e-15 1.88992637998508e-14 0.99999999999999 55 4.96267064433184e-14 9.92534128866368e-14 0.99999999999995 56 1.74945608561176e-13 3.49891217122351e-13 0.999999999999825 57 1.31732976425342e-13 2.63465952850685e-13 0.999999999999868 58 2.6271460542334e-13 5.2542921084668e-13 0.999999999999737 59 6.56779337128046e-13 1.31355867425609e-12 0.999999999999343 60 4.66649932884429e-12 9.33299865768858e-12 0.999999999995334 61 5.54372410410396e-12 1.10874482082079e-11 0.999999999994456 62 4.16214168968755e-12 8.3242833793751e-12 0.999999999995838 63 3.71848152287745e-12 7.43696304575489e-12 0.999999999996282 64 1.66456695203688e-12 3.32913390407375e-12 0.999999999998335 65 2.67517903859014e-12 5.35035807718028e-12 0.999999999997325 66 1.9977766087946e-12 3.9955532175892e-12 0.999999999998002 67 1.01899532140061e-12 2.03799064280123e-12 0.999999999998981 68 1.25240936053068e-12 2.50481872106135e-12 0.999999999998748 69 2.22244407568672e-12 4.44488815137345e-12 0.999999999997778 70 1.62147283821378e-12 3.24294567642757e-12 0.999999999998379 71 2.14945478500169e-12 4.29890957000337e-12 0.99999999999785 72 6.24750861711635e-12 1.24950172342327e-11 0.999999999993753 73 2.12683666382060e-11 4.25367332764120e-11 0.999999999978732 74 4.44022691880933e-10 8.88045383761867e-10 0.999999999555977 75 3.76812550044232e-08 7.53625100088463e-08 0.999999962318745 76 4.5712439170467e-07 9.1424878340934e-07 0.999999542875608 77 1.30333639608615e-06 2.6066727921723e-06 0.999998696663604 78 3.27635965240004e-06 6.55271930480008e-06 0.999996723640348 79 1.15111969440051e-05 2.30223938880102e-05 0.999988488803056 80 0.000386410847262092 0.000772821694524184 0.999613589152738 81 0.000978325671497921 0.00195665134299584 0.999021674328502 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 63 0.984375 NOK 5% type I error level 64 1 NOK 10% type I error level 64 1 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/104oc11229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/104oc11229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/1gg6v1229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/1gg6v1229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/27igi1229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/27igi1229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/3rmoj1229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/3rmoj1229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/45xe51229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/45xe51229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/5kg5n1229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/5kg5n1229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/626no1229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/626no1229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/76to61229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/76to61229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/84wgv1229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/84wgv1229183101.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/9ypcd1229183101.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229183164ollhd3n8uyul2al/9ypcd1229183101.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