MR - Cannotdo verklaren

*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: Sun, 19 Dec 2010 18:20:33 +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/19/t1292782741mg8rwyt8h7o28kd.htm/, Retrieved Sun, 19 Dec 2010 19:19:04 +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/19/t1292782741mg8rwyt8h7o28kd.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 «

0 1 4 4 2 0 1 2 2 2 0 1 5 5 4 1 1 4 5 3 0 2 1 1 2 0 1 2 4 1 0 4 5 6 4 0 1 1 5 3 0 1 3 4 1 0 2 5 5 4 1 1 2 7 4 0 1 2 2 4 1 2 2 7 3 0 1 2 5 4 0 1 1 5 1 1 1 4 7 4 1 1 3 3 1 0 1 6 6 4 1 1 1 2 4 0 2 3 6 3 1 1 2 1 2 0 2 5 5 6 0 1 5 4 5 0 2 3 4 4 1 1 3 7 6 0 1 5 7 1 1 1 5 5 2 0 2 4 6 4 1 1 2 5 4 0 1 1 1 1 1 2 4 6 2 0 1 6 4 1 0 1 2 2 2 1 1 3 2 2 1 1 2 6 2 1 2 4 6 6 1 1 2 6 2 0 1 1 1 1 1 1 5 6 4 1 1 5 6 3 0 1 1 1 3 1 1 1 1 1 1 1 2 7 4 0 1 4 2 3 0 1 5 3 4 0 1 3 5 3 0 1 3 3 2 1 1 1 4 1 0 1 2 2 5 1 1 3 3 4 1 2 2 7 1 0 2 5 7 2 1 1 4 5 4 0 1 4 1 3 0 1 2 2 2 0 2 3 5 3 1 1 6 2 3 0 1 2 4 2 1 2 3 7 2 1 1 2 2 4 0 1 5 5 4 0 1 5 6 2 0 1 5 3 2 1 1 6 7 5 0 2 4 4 4 1 1 2 3 5 0 1 5 5 5 1 2 2 3 2 1 1 1 2 3 0 1 6 6 4 0 1 6 6 2 1 1 3 5 2 1 1 4 2 2 0 3 5 3 5 0 2 2 4 2 0 2 4 6 3 1 1 3 5 2 1 1 2 2 2 1 1 2 5 2 1 1 3 2 2 0 1 3 1 2 1 1 7 2 1 0 1 2 4 3 0 1 2 5 3 1 1 2 5 3 0 1 5 3 3 0 1 1 2 1 0 3 5 7 4 0 1 2 1 1 0 1 1 5 1 0 1 2 5 1 0 1 2 2 3 0 1 0 6 2 0 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Cannotdo[t] = + 1.31587394220932 -0.0645747954209116Gender[t] + 0.365174200689959Depressed[t] + 0.173533918158284Worrytoomuch[t] + 0.270908114989842Limitactivity[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) 1.31587394220932 0.429921 3.0607 0.002625 0.001313 Gender -0.0645747954209116 0.258578 -0.2497 0.803144 0.401572 Depressed 0.365174200689959 0.226293 1.6137 0.108731 0.054366 Worrytoomuch 0.173533918158284 0.075471 2.2993 0.022894 0.011447 Limitactivity 0.270908114989842 0.098387 2.7535 0.006641 0.00332 Multiple Linear Regression - Regression Statistics Multiple R 0.375533136675947 R-squared 0.141025136741675 Adjusted R-squared 0.117651671074782 F-TEST (value) 6.03355697231615 F-TEST (DF numerator) 4 F-TEST (DF denominator) 147 p-value 0.000159641632979213 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.5383513758478 Sum Squared Residuals 347.879168469205 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4 2.91700004551209 1.08299995448791 2 2 2.56993220919553 -0.569932209195526 3 5 3.63235019365006 1.36764980634994 4 4 3.29686728323931 0.703132716760691 5 1 2.7615724917272 -1.7615724917272 6 2 2.64609193052225 -0.646091930522252 7 5 4.90140671387822 0.0985932861217778 8 1 3.36144207866022 -2.36144207866022 9 3 2.64609193052225 0.353908069477748 10 5 3.99752439434002 1.00247560565998 11 2 3.91484323454572 -1.91484323454572 12 2 3.11174843917521 -1.11174843917521 13 2 4.00910932024583 -2.00910932024583 14 2 3.63235019365006 -1.63235019365006 15 1 2.81962584868054 -1.81962584868054 16 4 3.91484323454572 0.0851567654542813 17 3 2.40798321694306 0.592016783056944 18 6 3.80588411180835 2.19411588819165 19 1 3.0471736437543 -2.0471736437543 20 3 3.90015019750846 -0.900150197508463 21 2 2.33182349561633 -0.33182349561633 22 5 4.53934062431971 0.460659375680295 23 5 3.72972439048162 1.27027560951838 24 3 3.82399047618174 -0.823990476181737 25 3 4.4566594645254 -1.4566594645254 26 5 3.1666936849971 1.8333063150029 27 5 3.02595916824947 1.97404083175053 28 4 4.1710583124983 -0.171058312498305 29 2 3.56777539822915 -1.56777539822915 30 1 2.1254901760474 -1.1254901760474 31 4 3.56466728709771 0.435332712902291 32 6 2.64609193052225 3.35390806947775 33 2 2.56993220919553 -0.569932209195526 34 3 2.50535741377461 0.494642586225386 35 2 3.19949308640775 -1.19949308640775 36 4 4.64829974705708 -0.648299747057078 37 2 3.19949308640775 -1.19949308640775 38 1 2.1254901760474 -1.1254901760474 39 5 3.74130931638743 1.25869068361257 40 5 3.47040120139759 1.52959879860241 41 1 2.66730640602708 -1.66730640602708 42 1 2.06091538062649 -1.06091538062649 43 2 3.91484323454572 -1.91484323454572 44 4 2.84084032418537 1.15915967581463 45 5 3.28528235733349 1.71471764266651 46 3 3.36144207866022 -0.36144207866022 47 3 2.74346612735381 0.25653387264619 48 1 2.58151713510134 -1.58151713510134 49 2 3.38265655416505 -1.38265655416505 50 3 3.22070756191258 -0.220707561912583 51 2 3.46729309026615 -1.46729309026615 52 5 3.8027760006769 1.1972239993231 53 4 3.56777539822915 0.432224601770849 54 4 2.66730640602708 1.33269359397292 55 2 2.56993220919553 -0.569932209195526 56 3 3.72661627935018 -0.726616279350179 57 6 2.77626552876446 3.22373447123554 58 2 2.91700004551209 -0.917000045512094 59 3 3.73820120525599 -0.738201205255993 60 2 3.0471736437543 -1.0471736437543 61 5 3.63235019365006 1.36764980634994 62 5 3.26406788182866 1.73593211817134 63 5 2.74346612735381 2.25653387264619 64 6 4.18575134953556 1.81424865046444 65 4 3.82399047618174 0.176009523818263 66 2 3.49161567690243 -1.49161567690243 67 5 3.9032583086399 1.0967416913601 68 2 3.04406553262286 -1.04406553262286 69 1 2.77626552876446 -1.77626552876446 70 6 3.80588411180835 2.19411588819165 71 6 3.26406788182866 2.73593211817134 72 3 3.02595916824947 -0.0259591682494662 73 4 2.50535741377461 1.49464258622539 74 5 4.28653887370325 0.713461126296746 75 2 3.28217424620205 -1.28217424620205 76 4 3.90015019750846 0.0998498024915373 77 3 3.02595916824947 -0.0259591682494662 78 2 2.50535741377461 -0.505357413774614 79 2 3.02595916824947 -1.02595916824947 80 3 2.50535741377461 0.494642586225386 81 3 2.39639829103724 0.603601708962758 82 7 2.23444929878477 4.76555070121523 83 2 3.18790816050194 -1.18790816050194 84 2 3.36144207866022 -1.36144207866022 85 2 3.29686728323931 -1.29686728323931 86 5 3.01437424234365 1.98562575765635 87 1 2.29902409420568 -1.29902409420568 88 5 4.70976643134655 0.290233568653452 89 2 2.1254901760474 -0.1254901760474 90 1 2.81962584868054 -1.81962584868054 91 2 2.81962584868054 -0.819625848680536 92 2 2.84084032418537 -0.840840324185368 93 0 3.26406788182866 -3.26406788182866 94 5 2.84084032418537 2.15915967581463 95 3 3.9032583086399 -0.903258308639905 96 2 3.01437424234365 -1.01437424234365 97 4 2.6788913319329 1.3211086680671 98 2 3.02595916824947 -1.02595916824947 99 2 3.36144207866022 -1.36144207866022 100 4 3.93294959891911 0.0670504010808907 101 1 3.80588411180835 -2.80588411180835 102 5 3.29686728323931 1.70313271676069 103 4 3.09053396367038 0.909466036329622 104 6 3.83557540208755 2.16442459791245 105 2 2.84084032418537 -0.840840324185368 106 5 3.93294959891911 1.06705040108089 107 1 3.39113336893942 -2.39113336893942 108 7 3.93947103738669 3.06052896261331 109 5 3.45570816436034 1.54429183563966 110 3 3.62924208251862 -0.62924208251862 111 4 3.80588411180835 0.194115888191654 112 4 3.49161567690243 0.508384323097575 113 2 2.20164989737413 -0.201649897374125 114 1 2.40798321694306 -1.40798321694306 115 6 4.17416642362975 1.82583357637025 116 4 2.81962584868054 1.18037415131946 117 2 2.56993220919553 -0.569932209195526 118 7 2.83773221305393 4.16226778694607 119 4 3.28528235733349 0.714717642666506 120 4 3.26406788182866 0.735932118171338 121 4 3.63235019365006 0.367649806349938 122 2 2.29902409420568 -0.299024094205684 123 5 3.45881627549178 1.54118372450822 124 3 2.77626552876446 0.223734471235544 125 2 2.29902409420568 -0.299024094205684 126 3 3.09053396367038 -0.0905339636703779 127 4 3.9032583086399 0.0967416913600956 128 5 3.48850756577098 1.51149243422902 129 6 3.39113336893942 2.60886663106058 130 2 2.39639829103724 -0.396398291037242 131 2 3.56777539822915 -1.56777539822915 132 2 3.56777539822915 -1.56777539822915 133 2 3.63235019365006 -1.63235019365006 134 2 4.83683191845731 -2.83683191845731 135 5 3.56777539822915 1.43222460177085 136 2 3.99752439434002 -1.99752439434002 137 3 3.26406788182866 -0.264067881828662 138 6 3.56777539822915 2.43222460177085 139 4 3.09053396367038 0.909466036329622 140 5 3.37302700456603 1.62697299543397 141 1 2.1254901760474 -1.1254901760474 142 2 2.94979944692274 -0.94979944692274 143 2 3.09053396367038 -1.09053396367038 144 2 2.81962584868054 -0.819625848680536 145 6 3.47040120139759 2.52959879860241 146 2 3.12333336508102 -1.12333336508102 147 2 2.56993220919553 -0.569932209195526 148 1 4.09489859117158 -3.09489859117158 149 5 3.02595916824947 1.97404083175053 150 3 3.9032583086399 -0.903258308639905 151 6 4.36269859502998 1.63730140497002 152 1 2.81962584868054 -1.81962584868054 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.7306930861405 0.538613827719 0.2693069138595 9 0.613990811887216 0.772018376225567 0.386009188112784 10 0.511037644824082 0.977924710351835 0.488962355175918 11 0.632678666206883 0.734642667586235 0.367321333793117 12 0.534625308246677 0.930749383506646 0.465374691753323 13 0.498894858809034 0.997789717618068 0.501105141190966 14 0.496060546362832 0.992121092725664 0.503939453637168 15 0.484183018845336 0.968366037690672 0.515816981154664 16 0.421898248346063 0.843796496692126 0.578101751653937 17 0.40146548053376 0.80293096106752 0.59853451946624 18 0.530842661774513 0.938314676450974 0.469157338225487 19 0.499182409957246 0.998364819914493 0.500817590042754 20 0.440122637752281 0.880245275504562 0.559877362247719 21 0.384675336945415 0.769350673890831 0.615324663054585 22 0.323944637856904 0.647889275713808 0.676055362143096 23 0.300730986294966 0.601461972589933 0.699269013705034 24 0.250808266355262 0.501616532710524 0.749191733644738 25 0.23262341030639 0.46524682061278 0.76737658969361 26 0.25355057287005 0.5071011457401 0.74644942712995 27 0.35004941956638 0.700098839132759 0.64995058043362 28 0.290371423261824 0.580742846523647 0.709628576738176 29 0.265384104126337 0.530768208252674 0.734615895873663 30 0.22845318096548 0.45690636193096 0.77154681903452 31 0.194776124641692 0.389552249283384 0.805223875358308 32 0.373050280954691 0.746100561909383 0.626949719045309 33 0.320343582390034 0.640687164780068 0.679656417609966 34 0.297361303781188 0.594722607562377 0.702638696218812 35 0.278007173827363 0.556014347654725 0.721992826172637 36 0.235582919953995 0.471165839907989 0.764417080046005 37 0.215291412875678 0.430582825751355 0.784708587124322 38 0.191338659240181 0.382677318480362 0.80866134075982 39 0.195155902967077 0.390311805934155 0.804844097032923 40 0.202473327592522 0.404946655185044 0.797526672407478 41 0.189777113348524 0.379554226697049 0.810222886651475 42 0.160212884707382 0.320425769414764 0.839787115292618 43 0.180065780466973 0.360131560933945 0.819934219533027 44 0.179280263831553 0.358560527663107 0.820719736168447 45 0.200490996458396 0.400981992916792 0.799509003541604 46 0.169126683724473 0.338253367448946 0.830873316275527 47 0.138256094229212 0.276512188458425 0.861743905770788 48 0.132533520054987 0.265067040109974 0.867466479945013 49 0.120823986454449 0.241647972908899 0.87917601354555 50 0.100422570780921 0.200845141561842 0.899577429219079 51 0.0950250228441136 0.190050045688227 0.904974977155886 52 0.0841990434811981 0.168398086962396 0.915800956518802 53 0.071153042278619 0.142306084557238 0.928846957721381 54 0.0726583689418691 0.145316737883738 0.927341631058131 55 0.0583975123314688 0.116795024662938 0.941602487668531 56 0.0475661584850437 0.0951323169700873 0.952433841514956 57 0.144862175160361 0.289724350320722 0.855137824839639 58 0.128764878416382 0.257529756832764 0.871235121583618 59 0.108003689396686 0.216007378793373 0.891996310603314 60 0.0932469549097622 0.186493909819524 0.906753045090238 61 0.0873600595681577 0.174720119136315 0.912639940431842 62 0.088711594032529 0.177423188065058 0.911288405967471 63 0.112534902784666 0.225069805569331 0.887465097215334 64 0.122765324511484 0.245530649022968 0.877234675488516 65 0.100551449174387 0.201102898348774 0.899448550825613 66 0.0964711677350253 0.192942335470051 0.903528832264975 67 0.0842236865322021 0.168447373064404 0.915776313467798 68 0.0733676013883317 0.146735202776663 0.926632398611668 69 0.0771348695470643 0.154269739094129 0.922865130452936 70 0.0926627331444097 0.185325466288819 0.90733726685559 71 0.139527709792436 0.279055419584872 0.860472290207564 72 0.114999114340665 0.22999822868133 0.885000885659335 73 0.124206344173549 0.248412688347098 0.875793655826451 74 0.11447809163372 0.22895618326744 0.88552190836628 75 0.107507993450925 0.21501598690185 0.892492006549075 76 0.0876121963722238 0.175224392744448 0.912387803627776 77 0.0703285869199061 0.140657173839812 0.929671413080094 78 0.0596146788459731 0.119229357691946 0.940385321154027 79 0.0528409331399687 0.105681866279937 0.947159066860031 80 0.0447620432883828 0.0895240865767655 0.955237956711617 81 0.0356125631482478 0.0712251262964956 0.964387436851752 82 0.21862918314987 0.437258366299739 0.78137081685013 83 0.208014687457807 0.416029374915615 0.791985312542193 84 0.203511130499038 0.407022260998076 0.796488869500962 85 0.196026487154942 0.392052974309885 0.803973512845058 86 0.216516052031089 0.433032104062178 0.783483947968911 87 0.209214255452644 0.418428510905288 0.790785744547356 88 0.179067422312009 0.358134844624017 0.820932577687991 89 0.149824098006263 0.299648196012525 0.850175901993737 90 0.161978318431215 0.32395663686243 0.838021681568785 91 0.142023275018047 0.284046550036095 0.857976724981953 92 0.124445955888567 0.248891911777134 0.875554044111433 93 0.224407525679599 0.448815051359199 0.7755924743204 94 0.258330287765318 0.516660575530636 0.741669712234682 95 0.231633229454359 0.463266458908718 0.768366770545641 96 0.209805402052721 0.419610804105442 0.79019459794728 97 0.195950855288672 0.391901710577344 0.804049144711328 98 0.181367322205196 0.362734644410392 0.818632677794804 99 0.172657235224158 0.345314470448315 0.827342764775842 100 0.144315747606418 0.288631495212837 0.855684252393582 101 0.216743164594465 0.433486329188931 0.783256835405535 102 0.215210963172654 0.430421926345308 0.784789036827346 103 0.189964045253383 0.379928090506767 0.810035954746617 104 0.211368080311182 0.422736160622364 0.788631919688818 105 0.185746538092621 0.371493076185241 0.814253461907379 106 0.164440483482394 0.328880966964789 0.835559516517606 107 0.220848198888246 0.441696397776491 0.779151801111754 108 0.378851588461791 0.757703176923581 0.62114841153821 109 0.37857934090343 0.75715868180686 0.62142065909657 110 0.338133005927575 0.67626601185515 0.661866994072425 111 0.290386663814521 0.580773327629043 0.709613336185479 112 0.251265562392714 0.502531124785428 0.748734437607286 113 0.21120168286112 0.422403365722239 0.78879831713888 114 0.222965022977074 0.445930045954148 0.777034977022926 115 0.283759527018609 0.567519054037217 0.716240472981391 116 0.250694968002392 0.501389936004785 0.749305031997608 117 0.210454842487563 0.420909684975126 0.789545157512437 118 0.578651064956978 0.842697870086044 0.421348935043022 119 0.568578832239112 0.862842335521776 0.431421167760888 120 0.518154768273986 0.963690463452028 0.481845231726014 121 0.474505934970339 0.949011869940679 0.52549406502966 122 0.414503492516727 0.829006985033454 0.585496507483273 123 0.493719535753498 0.987439071506996 0.506280464246502 124 0.430660739125689 0.861321478251379 0.569339260874311 125 0.370306785924391 0.740613571848783 0.629693214075609 126 0.310308862070252 0.620617724140505 0.689691137929748 127 0.292220823262574 0.584441646525147 0.707779176737426 128 0.28119242003726 0.56238484007452 0.71880757996274 129 0.350039378548435 0.70007875709687 0.649960621451565 130 0.314629576612384 0.629259153224768 0.685370423387616 131 0.336308533994156 0.672617067988312 0.663691466005844 132 0.395119034596215 0.79023806919243 0.604880965403785 133 0.368613402780725 0.73722680556145 0.631386597219275 134 0.621287724401456 0.757424551197089 0.378712275598544 135 0.558862796236478 0.882274407527045 0.441137203763522 136 0.570711672282726 0.858576655434548 0.429288327717274 137 0.477746818252417 0.955493636504834 0.522253181747583 138 0.54439313328315 0.911213733433699 0.455606866716849 139 0.547827828783647 0.904344342432706 0.452172171216353 140 0.445020894334302 0.890041788668604 0.554979105665698 141 0.380110235928674 0.760220471857348 0.619889764071326 142 0.297400402726462 0.594800805452925 0.702599597273538 143 0.193747838811773 0.387495677623547 0.806252161188227 144 0.110674239487989 0.221348478975977 0.889325760512011 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 3 0.0218978102189781 OK

Charts produced by software:

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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