multiple regression: olieprijs en dowjones

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 30 Dec 2009 11:30:16 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w.htm/, Retrieved Wed, 30 Dec 2009 19:31:43 +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/2009/Dec/30/t1262197891md360pp6g214b1w.htm/}, year = {2009}, } @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 = {2009}, 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 31,54 10433,56 32,43 10665,78 26,54 10666,71 25,85 10682,74 27,6 10777,22 25,71 10052,6 25,38 10213,97 28,57 10546,82 27,64 10767,2 25,36 10444,5 25,9 10314,68 26,29 9042,56 21,74 9220,75 19,2 9721,84 19,32 9978,53 19,82 9923,81 20,36 9892,56 24,31 10500,98 25,97 10179,35 25,61 10080,48 24,67 9492,44 25,59 8616,49 26,09 8685,4 28,37 8160,67 27,34 8048,1 24,46 8641,21 27,46 8526,63 30,23 8474,21 32,33 7916,13 29,87 7977,64 24,87 8334,59 25,48 8623,36 27,28 9098,03 28,24 9154,34 29,58 9284,73 26,95 9492,49 29,08 9682,35 28,76 9762,12 29,59 10124,63 30,7 10540,05 30,52 10601,61 32,67 10323,73 33,19 10418,4 37,13 10092,96 35,54 10364,91 37,75 10152,09 41,84 10032,8 42,94 10204,59 49,14 10001,6 44,61 10411,75 40,22 10673,38 44,23 10539,51 45,85 10723,78 53,38 10682,06 53,26 10283,19 51,8 10377,18 55,3 10486,64 57,81 10545,38 63,96 10554,27 63,77 10532,54 59,15 10324,31 56,12 10695,25 57,42 10827,81 63,52 10872,48 61,71 10971,19 63,01 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation olieprijs[t] = -55.9227838874668 + 0.0102687293582253dowjones[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) -55.9227838874668 14.489554 -3.8595 0.000193 9.6e-05 dowjones 0.0102687293582253 0.001373 7.4785 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.582326771814317 R-squared 0.339104469171683 Adjusted R-squared 0.333041207420965 F-TEST (value) 55.9277305043773 F-TEST (DF numerator) 1 F-TEST (DF denominator) 109 p-value 2.02758920764268e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 21.5100723324371 Sum Squared Residuals 50432.4700803879 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 56.7033047787316 -24.0233047787317 2 31.54 51.2166199953384 -19.6766199953384 3 32.43 53.6012243269055 -21.1712243269055 4 26.54 53.6107742452087 -27.0707742452087 5 25.85 53.775381976821 -27.9253819768210 6 27.6 54.7455715265862 -27.1455715265862 7 25.71 47.3046448590289 -21.5946448590289 8 25.38 48.9617097155657 -23.5817097155657 9 28.57 52.3796562824510 -23.8096562824510 10 27.64 54.6426788584168 -27.0026788584168 11 25.36 51.3289598945174 -25.9689598945174 12 25.9 49.9958734492326 -24.0958734492326 13 26.29 36.932817458047 -10.6428174580470 14 21.74 38.7626023423892 -17.0226023423892 15 19.2 43.9081599365023 -24.7081599365023 16 19.32 46.5440400754652 -27.2240400754652 17 19.82 45.9821352049831 -26.1621352049831 18 20.36 45.6612374125386 -25.3012374125386 19 24.31 51.90893772867 -27.59893772867 20 25.97 48.606206305184 -22.636206305184 21 25.61 47.5909370335362 -21.9809370335363 22 24.67 41.5525134217255 -16.8825134217254 23 25.59 32.557619940388 -6.96761994038798 24 26.09 33.2652380804633 -7.17523808046329 25 28.37 27.8769277243217 0.493072275678275 26 27.34 26.7209768604663 0.619023139533698 27 24.46 32.8114629301233 -8.3514629301233 28 27.46 31.6348719202579 -4.17487192025785 29 30.23 31.0965851272997 -0.866585127299676 30 32.33 25.3658126470613 6.9641873529387 31 29.87 25.9974421898857 3.87255781011425 32 24.87 29.6628651343043 -4.79286513430427 33 25.48 32.628166111079 -7.148166111079 34 27.28 37.5024238755478 -10.2224238755478 35 28.24 38.0806560257095 -9.84065602570947 36 29.58 39.4195956467285 -9.83959564672846 37 26.95 41.5530268581934 -14.6030268581934 38 29.08 43.502647814146 -14.4226478141460 39 28.76 44.3217843550517 -15.5617843550517 40 29.59 48.0443014347019 -18.4543014347019 41 30.7 52.3101369846959 -21.6101369846959 42 30.52 52.9422799639882 -22.4222799639882 43 32.67 50.0888054499246 -17.4188054499246 44 33.19 51.0609460582677 -17.8709460582678 45 37.13 47.7190907759269 -10.5890907759269 46 35.54 50.5116717248963 -14.9716717248963 47 37.75 48.3262807428788 -10.5762807428788 48 41.84 47.1013240177361 -5.26132401773606 49 42.94 48.8653890341856 -5.9253890341856 50 49.14 46.7809396617594 2.35906033824055 51 44.61 50.9926590080356 -6.38265900803556 52 40.22 53.679266670028 -13.4592666700280 53 44.23 52.3045918708424 -8.07459187084243 54 45.85 54.1968106296826 -8.3468106296826 55 53.38 53.7683992408574 -0.388399240857432 56 53.26 49.6725111617421 3.58748883825788 57 51.8 50.6376690341217 1.16233096587829 58 55.3 51.761684149673 3.53831585032695 59 57.81 52.3648693121752 5.44513068782481 60 63.96 52.4561583161698 11.5038416838302 61 63.77 52.2330188272156 11.5369811727844 62 59.15 50.0947613129523 9.05523868704767 63 56.12 53.9038437810924 2.21615621890757 64 57.42 55.2650665448188 2.15493345518123 65 63.52 55.7237706852507 7.7962293147493 66 61.71 56.7373969602011 4.97260303979887 67 63.01 58.5288794840371 4.48112051596289 68 68.18 59.4431044587999 8.7368955412001 69 72.03 60.4617624111359 11.5682375888641 70 69.75 57.0123935324144 12.7376064675856 71 74.41 57.4120524790365 16.9979475209635 72 74.33 59.6758965533509 14.6541034466491 73 64.24 62.512530351267 1.72746964873295 74 60.03 66.9232576725056 -6.89325767250557 75 59.44 69.2032236519123 -9.76322365191232 76 62.5 71.17964599149 -8.67964599148996 77 55.04 72.5686970117771 -17.5286970117771 78 58.34 73.786465626369 -15.4464656263690 79 61.92 70.0594303058012 -8.13943030580115 80 67.65 75.0528053308254 -7.40280533082536 81 67.68 81.7577721652786 -14.0777721652786 82 70.3 82.5018442945756 -12.2018442945756 83 75.26 84.4844278717682 -9.22442787176816 84 71.44 80.0322148839224 -8.5922148839224 85 76.36 83.297465445251 -6.93746544525089 86 81.71 86.8256981654435 -5.11569816544353 87 92.6 79.630399504135 12.9696004958649 88 90.6 81.7497625563792 8.85023744362083 89 92.23 72.8277770534851 19.4022229465149 90 94.09 71.6104191880675 22.4795808119325 91 102.79 69.2928696592096 33.4971303407904 92 109.65 74.0447241697284 35.6052758302716 93 124.05 75.6451056402078 48.4048943597922 94 132.69 67.8838973039675 64.8061026960325 95 135.81 60.3436720235163 75.4663279764837 96 116.07 62.4833671598897 53.5866328401103 97 101.42 58.2046956981979 43.2153043018021 98 75.73 38.3619165228313 37.3680834771687 99 55.48 32.537698605433 22.942301394567 100 43.8 32.3426954349203 11.4573045650797 101 45.29 30.2955215500645 14.9944784499355 102 44.01 23.0488792419649 20.9611207580351 103 47.48 18.3762993220917 29.1037006779083 104 51.07 26.1461333909928 24.9238666090072 105 57.84 30.3178046927719 27.5221953072281 106 69.04 32.3164074877633 36.7235925122367 107 65.61 33.2072197595893 32.4027802404107 108 72.87 40.3427544160329 32.5272455839671 109 68.41 43.0161154171533 25.3938845828467 110 73.25 45.2995727645419 27.9504272354581 111 77.43 49.2169903274112 28.2130096725888 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0076473633178198 0.0152947266356396 0.99235263668218 6 0.00139677209041784 0.00279354418083568 0.998603227909582 7 0.000218103298605027 0.000436206597210053 0.999781896701395 8 3.32870984615191e-05 6.65741969230383e-05 0.999966712901538 9 4.32657197371528e-06 8.65314394743056e-06 0.999995673428026 10 6.92490248508037e-07 1.38498049701607e-06 0.999999307509752 11 1.26088191084538e-07 2.52176382169077e-07 0.99999987391181 12 1.64548617706270e-08 3.29097235412541e-08 0.999999983545138 13 4.550569371878e-09 9.101138743756e-09 0.99999999544943 14 9.37401733802968e-10 1.87480346760594e-09 0.999999999062598 15 1.12902616565253e-09 2.25805233130505e-09 0.999999998870974 16 1.15807862414103e-09 2.31615724828206e-09 0.999999998841921 17 5.76541196062174e-10 1.15308239212435e-09 0.999999999423459 18 1.95853873718305e-10 3.91707747436610e-10 0.999999999804146 19 5.1722977849872e-11 1.03445955699744e-10 0.999999999948277 20 9.98217808795113e-12 1.99643561759023e-11 0.999999999990018 21 1.92118740985458e-12 3.84237481970915e-12 0.999999999998079 22 4.55409013107618e-13 9.10818026215236e-13 0.999999999999545 23 3.61328381794412e-13 7.22656763588824e-13 0.999999999999639 24 1.43009747196045e-13 2.8601949439209e-13 0.999999999999857 25 1.14679009239687e-13 2.29358018479374e-13 0.999999999999885 26 3.44480927679641e-14 6.88961855359282e-14 0.999999999999966 27 6.36734941660004e-15 1.27346988332001e-14 0.999999999999994 28 1.53534694571255e-15 3.07069389142509e-15 0.999999999999998 29 7.46472522238545e-16 1.49294504447709e-15 1 30 6.25466533341625e-16 1.25093306668325e-15 1 31 1.62785444003324e-16 3.25570888006649e-16 1 32 3.40522949395136e-17 6.81045898790272e-17 1 33 6.78905287725539e-18 1.35781057545108e-17 1 34 1.41447291898268e-18 2.82894583796535e-18 1 35 3.26386693957087e-19 6.52773387914173e-19 1 36 9.85942924229823e-20 1.97188584845965e-19 1 37 2.28736224777857e-20 4.57472449555714e-20 1 38 7.11322538701448e-21 1.42264507740290e-20 1 39 2.19129109195900e-21 4.38258218391801e-21 1 40 9.10430135843322e-22 1.82086027168664e-21 1 41 5.9031628504441e-22 1.18063257008882e-21 1 42 3.72804978087277e-22 7.45609956174554e-22 1 43 4.44116319768361e-22 8.88232639536722e-22 1 44 6.19926696783512e-22 1.23985339356702e-21 1 45 5.07095772714657e-21 1.01419154542931e-20 1 46 1.28548559514558e-20 2.57097119029116e-20 1 47 7.096191835121e-20 1.4192383670242e-19 1 48 2.06176048572089e-18 4.12352097144179e-18 1 49 4.14401562304348e-17 8.28803124608696e-17 1 50 6.78394683087695e-15 1.35678936617539e-14 0.999999999999993 51 4.48667893932478e-14 8.97335787864956e-14 0.999999999999955 52 8.38379644382117e-14 1.67675928876423e-13 0.999999999999916 53 3.07761764994792e-13 6.15523529989584e-13 0.999999999999692 54 1.21855543953909e-12 2.43711087907819e-12 0.999999999998781 55 2.12614648342259e-11 4.25229296684519e-11 0.999999999978739 56 2.20552249924672e-10 4.41104499849344e-10 0.999999999779448 57 9.90749169876725e-10 1.98149833975345e-09 0.99999999900925 58 5.57508166833243e-09 1.11501633366649e-08 0.999999994424918 59 3.06670519974533e-08 6.13341039949066e-08 0.999999969332948 60 2.90637534814009e-07 5.81275069628017e-07 0.999999709362465 61 1.47339520884296e-06 2.94679041768592e-06 0.999998526604791 62 3.45383644486776e-06 6.90767288973552e-06 0.999996546163555 63 4.83369788890461e-06 9.66739577780923e-06 0.999995166302111 64 6.49759962382277e-06 1.29951992476455e-05 0.999993502400376 65 1.14785785513790e-05 2.29571571027580e-05 0.999988521421449 66 1.52170140278202e-05 3.04340280556403e-05 0.999984782985972 67 1.84803065523026e-05 3.69606131046052e-05 0.999981519693448 68 2.58928908964950e-05 5.17857817929899e-05 0.999974107109104 69 3.82430394317363e-05 7.64860788634726e-05 0.999961756960568 70 5.19461668422504e-05 0.000103892333684501 0.999948053833158 71 8.2816094917897e-05 0.000165632189835794 0.999917183905082 72 0.000100291923631035 0.000200583847262071 0.999899708076369 73 7.70807049668336e-05 0.000154161409933667 0.999922919295033 74 6.33865769926826e-05 0.000126773153985365 0.999936613423007 75 5.78810847324065e-05 0.000115762169464813 0.999942118915268 76 5.08308886367328e-05 0.000101661777273466 0.999949169111363 77 8.07681623927217e-05 0.000161536324785443 0.999919231837607 78 0.000118622957190525 0.000237245914381051 0.99988137704281 79 0.000133251220305663 0.000266502440611325 0.999866748779694 80 0.00014322679343419 0.00028645358686838 0.999856773206566 81 0.000236850066513689 0.000473700133027377 0.999763149933486 82 0.000421011365266578 0.000842022730533156 0.999578988634733 83 0.000765332798712077 0.00153066559742415 0.999234667201288 84 0.0019286646575156 0.0038573293150312 0.998071335342484 85 0.00624262306377481 0.0124852461275496 0.993757376936225 86 0.0308153422426565 0.0616306844853129 0.969184657757344 87 0.0621753813976319 0.124350762795264 0.937824618602368 88 0.203766778858362 0.407533557716724 0.796233221141638 89 0.374561990793369 0.749123981586739 0.62543800920663 90 0.605430942304534 0.789138115390932 0.394569057695466 91 0.732707323770796 0.534585352458409 0.267292676229205 92 0.873792629852052 0.252414740295897 0.126207370147948 93 0.933114472959905 0.133771054080189 0.0668855270400947 94 0.962492506699994 0.0750149866000112 0.0375074933000056 95 0.999244725490263 0.00151054901947450 0.000755274509737249 96 0.999619218630353 0.000761562739294844 0.000380781369647422 97 0.999593499838218 0.000813000323564021 0.000406500161782011 98 0.999637467824319 0.000725064351362889 0.000362532175681444 99 0.99913115878141 0.00173768243718269 0.000868841218591343 100 0.999711540572145 0.000576918855710372 0.000288459427855186 101 0.999916042509363 0.000167914981273365 8.39574906366826e-05 102 0.99991262266919 0.000174754661621746 8.7377330810873e-05 103 0.999598346838513 0.000803306322973919 0.000401653161486959 104 0.999428550778122 0.00114289844375523 0.000571449221877614 105 0.999375255999146 0.00124948800170849 0.000624744000854243 106 0.997119369433465 0.00576126113307057 0.00288063056653528 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 91 0.892156862745098 NOK 5% type I error level 93 0.911764705882353 NOK 10% type I error level 95 0.931372549019608 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/10iunh1262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/10iunh1262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/19wz71262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/19wz71262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/2c0zm1262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/2c0zm1262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/36ibh1262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/36ibh1262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/4fe4q1262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/4fe4q1262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/5xvck1262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/5xvck1262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/6ot421262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/6ot421262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/7ts091262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/7ts091262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/8fncw1262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/8fncw1262197807.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/9m1031262197807.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262197891md360pp6g214b1w/9m1031262197807.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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') }

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