MR

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 24 Dec 2010 13:32:30 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197449u4ehrs4m9jxe9hr.htm/, Retrieved Fri, 24 Dec 2010 14:31:01 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197449u4ehrs4m9jxe9hr.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

4 1 27 5 26 49 35 4 1 36 4 25 45 34 5 1 25 4 17 54 13 2 1 27 3 37 36 35 3 2 25 3 35 36 28 5 2 44 3 15 53 32 4 1 50 4 27 46 35 4 1 41 4 36 42 36 4 1 48 5 25 41 27 4 2 43 4 30 45 29 5 2 47 2 27 47 27 4 2 41 3 33 42 28 3 1 44 2 29 45 29 4 2 47 5 30 40 28 3 2 40 3 25 45 30 3 2 46 3 23 40 25 4 1 28 3 26 42 15 3 1 56 3 24 45 33 4 2 49 4 35 47 31 2 2 25 4 39 31 37 4 2 41 4 23 46 37 3 2 26 3 32 34 34 4 1 50 5 29 43 32 4 1 47 4 26 45 21 3 1 52 2 21 42 25 3 2 37 5 35 51 32 2 2 41 3 23 44 28 4 1 45 4 21 47 22 5 2 26 4 28 47 25 4 1 0 3 30 41 26 2 1 52 4 21 44 34 5 1 46 2 29 51 34 4 1 58 3 28 46 36 3 1 54 5 19 47 36 4 1 29 3 26 46 26 2 2 50 3 33 38 26 3 1 43 2 34 50 34 3 2 30 3 33 48 33 3 2 47 2 40 36 31 5 1 45 3 24 51 33 0 2 48 1 35 35 22 4 2 48 3 35 49 29 4 2 26 4 32 38 24 4 1 46 5 20 47 37 2 2 0 3 35 36 32 4 2 50 3 35 47 23 3 1 25 4 21 46 29 4 1 47 2 33 43 35 1 2 47 2 40 53 20 2 1 41 3 22 55 28 2 2 45 2 35 39 26 4 2 41 4 20 55 36 3 2 45 5 28 41 26 4 2 40 3 46 33 33 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 14 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Teamwork33[t] = -0.627811222048486 + 0.000559943079122198geslacht[t] -0.00134553113394833leeftijd[t] + 0.096198517648116opleiding[t] + 0.0166751551093951Neuroticisme[t] + 0.0635975737699658Extraversie[t] + 0.0110399029138406Openheid[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) -0.627811222048486 0.816352 -0.769 0.442832 0.221416 geslacht 0.000559943079122198 0.149191 0.0038 0.997009 0.498505 leeftijd -0.00134553113394833 0.006477 -0.2077 0.835665 0.417832 opleiding 0.096198517648116 0.078721 1.222 0.223232 0.111616 Neuroticisme 0.0166751551093951 0.010854 1.5363 0.126143 0.063072 Extraversie 0.0635975737699658 0.012489 5.0923 1e-06 0 Openheid 0.0110399029138406 0.014623 0.755 0.451217 0.225608 Multiple Linear Regression - Regression Statistics Multiple R 0.386354784279872 R-squared 0.149270019335946 Adjusted R-squared 0.122119062506242 F-TEST (value) 5.49778117479238 F-TEST (DF numerator) 6 F-TEST (DF denominator) 188 p-value 2.87937910941061e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.00036654065481 Sum Squared Residuals 188.137844544393 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4 3.75364371821155 0.246356281788452 2 4 3.36323006725487 0.636769932745128 3 5 3.58516987159218 1.41483012840782 4 2 2.91790493010918 -0.917904930109179 5 3 2.81052630484053 0.189473695159474 6 5 3.57677647685239 1.42322352314761 7 4 3.45238041828219 0.547619581717807 8 4 3.37121620230626 0.628783797693738 9 4 3.11161259581886 0.888387404181138 10 4 3.38254755337413 0.61745244662587 11 5 3.23985826992617 1.76014173007383 12 4 3.13723293909836 0.862767060901642 13 3 3.17156988875543 -0.171569888755432 14 4 3.14433617472278 0.855663825277217 15 3 3.21804975649472 -0.218049756494724 16 3 2.80343887605321 0.196561123946788 17 4 2.89392007711487 1.10607992288513 18 3 3.21240586890456 -0.212405868904555 19 4 3.60712509548503 0.392874904514971 20 2 2.75479670030096 -0.754796700300959 21 4 3.42042932695695 0.579570673043048 22 3 2.69819957832150 0.301800421678495 23 4 3.35801681609768 0.64198318390232 24 4 3.22158564201091 0.77841435798909 25 3 2.79245206584342 0.207547934156575 26 3 3.98490018473423 -0.984900184734229 27 2 3.09767653554434 -1.09767653554434 28 4 3.2791359791856 0.720864020814397 29 5 3.45510680831703 1.54489319168297 30 4 3.05613692758528 0.943863072414716 31 2 3.21140337490415 -1.21140337490415 32 5 3.60566378367653 1.39433621632347 33 4 3.37313270958573 0.626867290414273 34 3 3.48443304720316 -0.484433047203162 35 4 3.26840377311303 0.731596226886968 36 2 2.84865305798528 -0.848653057985278 37 3 3.62947857885539 -0.629478578855389 38 3 3.58881873876079 -0.588818738760788 39 3 2.80122158653404 0.198778413465955 40 5 3.60879215399778 1.39120784600222 41 0 2.45734506221047 -2.45734506221047 42 4 3.61738745068311 0.382612549316889 43 4 2.93838936191108 1.06161063808892 44 4 3.52291235429798 0.477087645702016 45 2 2.8883241948446 -0.888324194844597 46 4 3.42126182339224 0.57873817660776 47 3 3.31972834849149 -0.319728348491487 48 4 3.17327818573428 0.82672181426572 49 1 3.76094140857122 -2.76094140857122 50 2 3.78001474882545 -1.78001474882545 51 2 2.85613007999566 -0.85613007999566 52 4 3.93174212264462 0.0682578773553817 53 3 3.15519469471417 -0.155194694714174 54 4 2.83817683729395 1.16182316270605 55 3 3.60174658959194 -0.601746589591941 56 3 3.16666188904272 -0.166661889042723 57 5 4.04143653479242 0.958563465207578 58 3 3.32070047122651 -0.320700471226513 59 2 2.58354337094263 -0.583543370942629 60 1 3.53321023251919 -2.53321023251919 61 2 3.23052946146232 -1.23052946146232 62 5 3.14960688003005 1.85039311996995 63 4 3.69525708542449 0.304742914575507 64 4 3.48569431153061 0.514305688469387 65 3 3.64495012833058 -0.644950128330581 66 4 3.63579622642499 0.364203773575012 67 4 3.27864141223836 0.721358587761636 68 2 2.95253250674872 -0.952532506748719 69 3 3.07083049868552 -0.0708304986855244 70 4 3.00105726309605 0.998942736903952 71 3 2.94520415683054 0.0547958431694561 72 2 2.40128891354019 -0.401288913540193 73 4 2.52506036845561 1.47493963154439 74 4 3.41099612317589 0.589003876824111 75 3 2.87827726911463 0.121722730885366 76 5 4.13673403600211 0.863265963997893 77 1 2.61092628989287 -1.61092628989287 78 3 3.73832572235812 -0.738325722358117 79 3 4.06327627005132 -1.06327627005132 80 5 3.76722888374438 1.23277111625562 81 2 3.38412736487840 -1.38412736487840 82 3 2.55580809691126 0.444191903088743 83 3 3.74968470747056 -0.749684707470564 84 4 3.63513813827683 0.364861861723174 85 2 2.99149131654039 -0.991491316540394 86 4 3.78426687945417 0.215733120545827 87 3 2.65876855975598 0.341231440244022 88 3 3.09770696706006 -0.0977069670600576 89 3 3.22805600383106 -0.228056003831056 90 2 3.76185677584723 -1.76185677584723 91 3 3.63001282029384 -0.630012820293843 92 2 3.09090756665300 -1.09090756665300 93 4 2.87054152450221 1.12945847549779 94 4 3.46454824769335 0.535451752306653 95 2 3.42974985231713 -1.42974985231713 96 1 2.52580681682443 -1.52580681682443 97 5 3.2594608042237 1.7405391957763 98 4 3.121204048759 0.878795951241001 99 4 3.58657601761264 0.413423982387363 100 4 3.7395784115413 0.260421588458703 101 3 3.00860882731488 -0.00860882731488219 102 3 3.25408229833061 -0.254082298330611 103 1 2.49944736682413 -1.49944736682413 104 5 3.17983894432500 1.82016105567500 105 3 3.35390698895573 -0.353906988955732 106 3 3.37153522425452 -0.371535224254524 107 2 2.79459903959432 -0.794599039594317 108 4 3.49253292637918 0.507467073620822 109 4 2.97133359842930 1.02866640157070 110 3 3.73719630685801 -0.737196306858013 111 4 2.83947745106876 1.16052254893124 112 4 3.25858028994318 0.741419710056822 113 2 2.53987277880064 -0.539872778800642 114 3 2.95608537101353 0.0439146289864661 115 3 3.7157707752796 -0.715770775279602 116 3 3.62629693309924 -0.626296933099238 117 4 3.18436206433501 0.815637935664988 118 5 2.38488904745677 2.61511095254323 119 3 3.28065199518145 -0.280651995181448 120 3 3.26004845610290 -0.260048456102898 121 2 2.49907239445033 -0.499072394450325 122 3 3.28377844945346 -0.283778449453464 123 1 1.30155966924746 -0.301559669247457 124 4 3.11248459718527 0.887515402814727 125 4 3.44025394981764 0.55974605018236 126 4 2.61358398816806 1.38641601183194 127 3 3.63077817620244 -0.630778176202442 128 5 3.87194607159445 1.12805392840555 129 2 3.59680481356183 -1.59680481356183 130 2 3.29102684628615 -1.29102684628615 131 3 3.01470838295106 -0.0147083829510622 132 3 3.37380048411051 -0.373800484110505 133 2 3.51245628936445 -1.51245628936445 134 1 3.3093877721919 -2.30938777219190 135 3 3.08790880754384 -0.0879088075438377 136 5 3.61914191323911 1.38085808676089 137 4 3.21723001413547 0.782769985864535 138 4 3.30670754773422 0.693292452265785 139 4 3.23495700208418 0.76504299791582 140 3 3.16176574378456 -0.161765743784560 141 5 2.99522979016784 2.00477020983216 142 3 3.26072750645867 -0.260727506458674 143 3 2.97618844518762 0.0238115548123835 144 3 3.17958283646325 -0.179582836463246 145 3 3.38795725170416 -0.387957251704158 146 4 3.32950398182521 0.670496018174786 147 2 3.06873231644301 -1.06873231644301 148 2 1.67643759723356 0.32356240276644 149 4 2.66570577972329 1.33429422027671 150 3 2.77638525480916 0.223614745190840 151 3 3.55472342364738 -0.554723423647376 152 2 2.92830572717015 -0.928305727170146 153 3 3.19357053542211 -0.193570535422113 154 3 3.40556090593604 -0.405560905936042 155 4 3.51328025816389 0.486719741836113 156 1 2.70783055026909 -1.70783055026909 157 1 3.19984171439962 -2.19984171439962 158 5 3.26822156901902 1.73177843098098 159 4 3.45145687566127 0.54854312433873 160 3 3.23016669561742 -0.230166695617418 161 3 3.25380016812417 -0.253800168124167 162 4 3.42549349524823 0.574506504751773 163 3 3.48901795556219 -0.489017955562189 164 2 2.60178464202460 -0.601784642024597 165 1 2.77973896192742 -1.77973896192742 166 1 3.03803355103566 -2.03803355103566 167 5 4.00680576655662 0.99319423344338 168 4 3.7227552977525 0.277244702247502 169 3 3.20311190091086 -0.203111900910859 170 4 3.60446440448251 0.395535595517493 171 5 3.6035054738443 1.3964945261557 172 4 3.59609186419156 0.403908135808436 173 4 2.40500779346212 1.59499220653788 174 2 3.34211939143489 -1.34211939143489 175 3 3.30298915510907 -0.302989155109067 176 4 3.31201424863965 0.687985751360353 177 3 3.04548299925180 -0.0454829992518048 178 4 3.37568444856883 0.624315551431166 179 3 3.3783165802092 -0.378316580209203 180 4 3.05142726928232 0.948572730717685 181 1 3.17262874550607 -2.17262874550607 182 2 3.32924153260512 -1.32924153260512 183 3 3.74283318327879 -0.742833183278789 184 3 3.35989410893563 -0.359894108935633 185 5 3.8294360692716 1.17056393072840 186 4 4.00527605502908 -0.0052760550290807 187 3 3.8861961539305 -0.886196153930499 188 3 3.38824036747852 -0.388240367478520 189 3 2.92125020637601 0.0787497936239947 190 3 3.1698491899915 -0.169849189991498 191 4 3.08422824811617 0.915771751883827 192 3 3.15805426554439 -0.158054265544392 193 2 2.62202281304329 -0.622022813043293 194 4 3.04471695717748 0.955283042822517 195 2 3.40359518010176 -1.40359518010176 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.112036880385157 0.224073760770313 0.887963119614843 11 0.0625846745255338 0.125169349051068 0.937415325474466 12 0.0235730748225195 0.0471461496450391 0.97642692517748 13 0.0353953665122981 0.0707907330245961 0.964604633487702 14 0.0180466631454041 0.0360933262908081 0.981953336854596 15 0.0404879029790977 0.0809758059581955 0.959512097020902 16 0.0238242709513799 0.0476485419027599 0.97617572904862 17 0.0178164721781209 0.0356329443562419 0.98218352782188 18 0.0144192487342075 0.028838497468415 0.985580751265792 19 0.0143795842116454 0.0287591684232908 0.985620415788355 20 0.00779134650030548 0.0155826930006110 0.992208653499695 21 0.00406679132964946 0.00813358265929891 0.99593320867035 22 0.00332825110619069 0.00665650221238138 0.99667174889381 23 0.00174649899495672 0.00349299798991344 0.998253501005043 24 0.000901623214715882 0.00180324642943176 0.999098376785284 25 0.000441041811162388 0.000882083622324776 0.999558958188838 26 0.00530086357179926 0.0106017271435985 0.9946991364282 27 0.0284795343910252 0.0569590687820504 0.971520465608975 28 0.0196212287604448 0.0392424575208897 0.980378771239555 29 0.0231513986589669 0.0463027973179339 0.976848601341033 30 0.0186733332107959 0.0373466664215917 0.981326666789204 31 0.0355360461621298 0.0710720923242597 0.96446395383787 32 0.0410922875092839 0.0821845750185679 0.958907712490716 33 0.0320636227918276 0.0641272455836552 0.967936377208172 34 0.0248461054126193 0.0496922108252386 0.97515389458738 35 0.0175582567995259 0.0351165135990519 0.982441743200474 36 0.0209910611129169 0.0419821222258338 0.979008938887083 37 0.0261112865279591 0.0522225730559182 0.97388871347204 38 0.0273918469604899 0.0547836939209799 0.97260815303951 39 0.0206803455586323 0.0413606911172646 0.979319654441368 40 0.0231299172548100 0.0462598345096201 0.97687008274519 41 0.114881094126568 0.229762188253136 0.885118905873432 42 0.0909181330105346 0.181836266021069 0.909081866989465 43 0.0872046854735151 0.174409370947030 0.912795314526485 44 0.0688286343227982 0.137657268645596 0.931171365677202 45 0.0700706497820871 0.140141299564174 0.929929350217913 46 0.055445175507327 0.110890351014654 0.944554824492673 47 0.0557605487224362 0.111521097444872 0.944239451277564 48 0.0571608696453258 0.114321739290652 0.942839130354674 49 0.327290029358461 0.654580058716922 0.672709970641539 50 0.519502942179304 0.960994115641391 0.480497057820696 51 0.491907632542959 0.983815265085919 0.508092367457041 52 0.449073008095229 0.898146016190457 0.550926991904771 53 0.41130712231627 0.82261424463254 0.58869287768373 54 0.476589295055445 0.95317859011089 0.523410704944555 55 0.481370391015927 0.962740782031853 0.518629608984073 56 0.448129143285983 0.896258286571966 0.551870856714017 57 0.430425255032624 0.860850510065248 0.569574744967376 58 0.388559666884037 0.777119333768074 0.611440333115963 59 0.364639562868932 0.729279125737863 0.635360437131068 60 0.599042569721052 0.801914860557896 0.400957430278948 61 0.609418784404668 0.781162431190663 0.390581215595332 62 0.707424535847167 0.585150928305666 0.292575464152833 63 0.673578172656878 0.652843654686244 0.326421827343122 64 0.641518524137119 0.716962951725762 0.358481475862881 65 0.635944262374969 0.728111475250062 0.364055737625031 66 0.60042229457922 0.79915541084156 0.39957770542078 67 0.580942664356578 0.838114671286843 0.419057335643422 68 0.573001182720482 0.853997634559037 0.426998817279519 69 0.529529653271858 0.940940693456285 0.470470346728142 70 0.519275609341127 0.961448781317747 0.480724390658873 71 0.47667428860297 0.95334857720594 0.52332571139703 72 0.448464644902324 0.896929289804648 0.551535355097676 73 0.475211181108169 0.950422362216337 0.524788818891831 74 0.44588374266791 0.89176748533582 0.55411625733209 75 0.404226384496527 0.808452768993055 0.595773615503473 76 0.386423392606508 0.772846785213015 0.613576607393492 77 0.481452512597794 0.962905025195587 0.518547487402206 78 0.466731797662257 0.933463595324513 0.533268202337743 79 0.470589619044884 0.94117923808977 0.529410380955116 80 0.489847558562115 0.97969511712423 0.510152441437885 81 0.555314086061354 0.889371827877291 0.444685913938646 82 0.524847579721512 0.950304840556976 0.475152420278488 83 0.508214154721044 0.983571690557913 0.491785845278956 84 0.470414370753996 0.940828741507991 0.529585629246004 85 0.463152724140658 0.926305448281315 0.536847275859342 86 0.426312076613283 0.852624153226566 0.573687923386717 87 0.39443003003702 0.78886006007404 0.60556996996298 88 0.356886719879991 0.713773439759983 0.643113280120009 89 0.323835288672047 0.647670577344095 0.676164711327953 90 0.408569487603882 0.817138975207764 0.591430512396118 91 0.390957496871791 0.781914993743581 0.609042503128209 92 0.40085226480663 0.80170452961326 0.59914773519337 93 0.417611611095762 0.835223222191524 0.582388388904238 94 0.389638336198202 0.779276672396404 0.610361663801798 95 0.429214495504057 0.858428991008114 0.570785504495943 96 0.487148838639707 0.974297677279413 0.512851161360293 97 0.568699346111628 0.862601307776744 0.431300653888372 98 0.558599981834819 0.882800036330362 0.441400018165181 99 0.527002765284521 0.945994469430958 0.472997234715479 100 0.486991885735866 0.973983771471732 0.513008114264134 101 0.445697828188158 0.891395656376317 0.554302171811842 102 0.408306742776345 0.81661348555269 0.591693257223655 103 0.462922171297335 0.925844342594671 0.537077828702665 104 0.558620029787678 0.882759940424645 0.441379970212323 105 0.52133320290777 0.95733359418446 0.47866679709223 106 0.484884541100265 0.96976908220053 0.515115458899735 107 0.472105891636894 0.944211783273788 0.527894108363106 108 0.450200897269663 0.900401794539326 0.549799102730337 109 0.45239814307671 0.90479628615342 0.54760185692329 110 0.428961089380562 0.857922178761124 0.571038910619438 111 0.438339887499767 0.876679774999534 0.561660112500233 112 0.419081375747914 0.838162751495828 0.580918624252086 113 0.391987883649752 0.783975767299504 0.608012116350248 114 0.351828826751346 0.703657653502692 0.648171173248654 115 0.330274127545393 0.660548255090786 0.669725872454607 116 0.306958869467000 0.613917738933999 0.693041130533 117 0.296821722355837 0.593643444711674 0.703178277644163 118 0.548750713048137 0.902498573903727 0.451249286951863 119 0.507741171118543 0.984517657762913 0.492258828881457 120 0.468309757159054 0.936619514318108 0.531690242840946 121 0.439383484983783 0.878766969967567 0.560616515016217 122 0.400538371299393 0.801076742598786 0.599461628700607 123 0.364280765298103 0.728561530596206 0.635719234701897 124 0.354289797447686 0.708579594895373 0.645710202552314 125 0.328755580118341 0.657511160236682 0.671244419881659 126 0.353568524791452 0.707137049582905 0.646431475208548 127 0.330200277355354 0.660400554710707 0.669799722644646 128 0.361561591456849 0.723123182913698 0.638438408543151 129 0.425015784575308 0.850031569150616 0.574984215424692 130 0.44830154714973 0.89660309429946 0.55169845285027 131 0.405352179513682 0.810704359027363 0.594647820486318 132 0.367111474769203 0.734222949538406 0.632888525230797 133 0.423458694843062 0.846917389686123 0.576541305156938 134 0.617068815165195 0.76586236966961 0.382931184834805 135 0.572599263566637 0.854801472866727 0.427400736433363 136 0.595221006813178 0.809557986373645 0.404778993186822 137 0.57159391148149 0.85681217703702 0.42840608851851 138 0.567933347211353 0.864133305577293 0.432066652788647 139 0.556383125668466 0.887233748663067 0.443616874331534 140 0.51073758427954 0.97852483144092 0.48926241572046 141 0.661232859646883 0.677534280706233 0.338767140353116 142 0.617683677553994 0.764632644892013 0.382316322446006 143 0.570194947270833 0.859610105458334 0.429805052729167 144 0.536557641598339 0.926884716803322 0.463442358401661 145 0.493751480878302 0.987502961756603 0.506248519121698 146 0.491879211082434 0.983758422164868 0.508120788917566 147 0.467335084695471 0.934670169390943 0.532664915304529 148 0.423037639866492 0.846075279732985 0.576962360133508 149 0.504273453290983 0.991453093418035 0.495726546709017 150 0.4862557345751 0.9725114691502 0.5137442654249 151 0.459886207304272 0.919772414608545 0.540113792695728 152 0.43820900944228 0.87641801888456 0.56179099055772 153 0.409610954485028 0.819221908970057 0.590389045514972 154 0.363852496819119 0.727704993638238 0.636147503180881 155 0.321358927936777 0.642717855873554 0.678641072063223 156 0.358479753133414 0.716959506266828 0.641520246866586 157 0.536754593308971 0.926490813382057 0.463245406691029 158 0.614922792381728 0.770154415236544 0.385077207618272 159 0.576441650052702 0.847116699894596 0.423558349947298 160 0.524619573861189 0.950760852277622 0.475380426138811 161 0.468841194840063 0.937682389680127 0.531158805159937 162 0.418358458170066 0.836716916340131 0.581641541829934 163 0.367290602853871 0.734581205707743 0.632709397146129 164 0.320504517852780 0.641009035705559 0.67949548214722 165 0.386632504283245 0.773265008566489 0.613367495716755 166 0.656667490938464 0.686665018123072 0.343332509061536 167 0.68308766197124 0.63382467605752 0.31691233802876 168 0.636472419634428 0.727055160731144 0.363527580365572 169 0.588655852577149 0.822688294845702 0.411344147422851 170 0.52613704852095 0.9477259029581 0.47386295147905 171 0.570059223762555 0.85988155247489 0.429940776237445 172 0.573852734307381 0.852294531385238 0.426147265692619 173 0.634085365830326 0.731829268339348 0.365914634169674 174 0.719595774613804 0.560808450772391 0.280404225386196 175 0.663346542229788 0.673306915540424 0.336653457770212 176 0.640411006798232 0.719177986403537 0.359588993201768 177 0.606955541707567 0.786088916584867 0.393044458292433 178 0.665838375134629 0.668323249730741 0.334161624865371 179 0.748181089925157 0.503637820149686 0.251818910074843 180 0.87763384241086 0.244732315178279 0.122366157589139 181 0.840986670493359 0.318026659013283 0.159013329506641 182 0.785098513453357 0.429802973093285 0.214901486546643 183 0.6711384900931 0.6577230198138 0.3288615099069 184 0.609055882181824 0.781888235636352 0.390944117818176 185 0.505487959620058 0.989024080759885 0.494512040379942 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0284090909090909 NOK 5% type I error level 21 0.119318181818182 NOK 10% type I error level 29 0.164772727272727 NOK

Charts produced by software:

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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

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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.

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