Multiple Regression 1

*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 21:52:44 +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/t1293227520qiiigwaiiepm1g3.htm/, Retrieved Fri, 24 Dec 2010 22:52:12 +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/t1293227520qiiigwaiiepm1g3.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 «

12 2 53 10 15 2 7 6 2 11 2 86 12 15 4 5 6 1 14 4 66 11 14 7 7 11 4 12 3 67 10 10 3 3 7 1 21 4 76 12 10 7 7 12 5 12 3 78 12 12 2 7 8 1 22 3 53 14 18 7 7 7 1 11 4 80 14 12 2 1 11 1 10 3 74 11 14 1 4 8 1 13 4 76 11 18 2 5 9 1 10 3 79 13 9 6 6 9 2 8 2 54 11 11 1 4 6 1 15 3 67 10 11 1 7 9 3 10 3 87 14 17 1 6 5 1 14 3 58 14 8 2 2 9 1 14 2 75 12 16 2 2 4 1 11 3 88 11 21 2 6 9 1 10 2 64 10 24 1 7 6 1 13 4 57 12 21 7 5 8 2 7 5 66 10 14 1 2 12 4 12 3 54 14 7 2 7 7 1 14 3 56 12 18 4 4 8 2 11 1 86 13 18 2 5 3 1 9 4 80 13 13 1 5 9 2 11 3 76 12 11 1 5 7 3 15 4 69 14 13 5 3 9 1 13 3 67 11 13 2 5 9 1 9 3 80 12 18 1 1 7 1 15 1 54 13 14 3 1 5 1 10 4 71 11 12 1 3 8 1 11 4 84 11 9 2 2 7 2 13 2 74 14 12 5 3 6 1 8 2 71 12 8 2 2 6 1 20 1 63 13 5 6 5 4 1 12 3 71 11 10 4 2 8 1 10 4 76 13 11 1 3 8 1 10 1 69 13 11 3 4 3 1 9 3 74 13 12 6 6 8 1 14 3 75 12 12 7 2 9 2 8 2 54 14 15 4 7 6 1 14 4 52 14 12 1 6 9 2 11 3 69 8 16 5 5 5 1 13 3 68 13 14 3 3 8 1 11 2 75 11 17 2 3 6 2 11 3 75 13 10 2 4 9 1 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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Depressie[t] = + 8.59793305408271 + 0.0318426073277617Leeftijd[t] -0.08663982982343Sportgerelateerde_groep[t] + 0.447620146494133Stress[t] + 0.0387187535662257Verwachtingen_ouders[t] + 0.349162354351205Slaapgebrek[t] + 0.196790971528799Veranderingen_verleden[t] + 0.297703798450042Alcoholgebruik[t] -0.140133754998486Rookgedrag[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) 8.59793305408271 2.86168 3.0045 0.003168 0.001584 Leeftijd 0.0318426073277617 0.388124 0.082 0.934734 0.467367 Sportgerelateerde_groep -0.08663982982343 0.023467 -3.6919 0.000321 0.000161 Stress 0.447620146494133 0.160235 2.7935 0.005967 0.002983 Verwachtingen_ouders 0.0387187535662257 0.069104 0.5603 0.576199 0.2881 Slaapgebrek 0.349162354351205 0.130757 2.6703 0.008502 0.004251 Veranderingen_verleden 0.196790971528799 0.136999 1.4364 0.153174 0.076587 Alcoholgebruik 0.297703798450042 0.16957 1.7556 0.081402 0.040701 Rookgedrag -0.140133754998486 0.261671 -0.5355 0.593155 0.296577 Multiple Linear Regression - Regression Statistics Multiple R 0.504839149613103 R-squared 0.254862566982081 Adjusted R-squared 0.211030953275144 F-TEST (value) 5.81458325231016 F-TEST (DF numerator) 8 F-TEST (DF denominator) 136 p-value 2.20929847349893e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.81913157803836 Sum Squared Residuals 1080.86038818385 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12 12.7085068466385 -0.708506846638536 2 11 11.1895092760968 -0.189509276096831 3 14 15.0088389205265 -1.00883892052653 4 12 11.3336340902916 0.66636590970841 5 21 14.5927557979730 6.40724420202697 6 12 12.0889790925686 -0.0889790925686139 7 22 16.8306356258460 5.16936437415404 8 11 12.5551478994151 -1.55514789941511 9 10 11.1258205035630 -1.12582050356305 10 13 11.9829155898389 1.01708441016110 11 10 13.6912316378682 -3.69123163786822 12 8 12.1152106351051 -4.11521063510513 13 15 11.7763321081737 3.22366789182629 14 10 10.958989963747 -0.95898996374701 15 14 13.8758899085666 0.124110091433375 16 14 10.2971609375319 3.70283906246812 17 11 11.2243422568575 -0.224342256857453 18 10 11.8949089013240 -1.89490890132403 19 13 15.5008229819844 -2.50082298198437 20 7 11.811836196059 -4.81183619605899 21 12 14.5722777350380 -2.57227773503803 22 14 14.1951859091990 -0.195185909198954 23 11 10.1300069719093 0.869993028090667 24 9 11.8487066863526 -2.84870668635263 25 11 10.9028243927934 0.0971756072065696 26 15 14.3925661902502 0.607433809749803 27 13 12.5372376830909 0.46276231690912 28 9 10.3203999723451 -1.32039997234507 29 15 12.9050125771303 2.09498742286972 30 10 11.1433541216999 -1.14335412169985 31 11 9.61541390267046 1.38458609732954 32 13 12.9638516775612 0.0361483224388247 33 8 10.9293778251959 -2.92937782519588 34 20 13.3137324773422 6.68626752265785 35 12 11.8847700987645 0.115229901235545 36 10 11.5666765120047 -1.56667651200474 37 10 11.4842241867665 -1.48422418676647 38 9 14.0830170042325 -5.08301700423249 39 14 13.2683255396025 0.731674460397511 40 8 15.2508060664924 -7.25080606649244 41 14 14.8803142858654 -0.880314285865371 42 11 10.9939257139137 0.0060742860862548 43 13 13.0424335126655 -0.0424335126655072 44 11 10.5403243580344 0.459675641965627 45 11 12.4264121052642 -1.42641210526423 46 10 11.4817469959667 -1.48174699596670 47 14 10.2060254599884 3.79397454001156 48 18 13.8234568840793 4.17654311592074 49 14 13.3171441517809 0.68285584821908 50 11 14.9339894199318 -3.93398941993175 51 12 12.0502839126430 -0.0502839126429844 52 13 12.3396602672548 0.660339732745223 53 9 13.5828832822197 -4.58288328221972 54 10 12.6598039520788 -2.65980395207876 55 15 12.7594759766291 2.24052402337086 56 20 13.9802088047325 6.01979119526755 57 12 12.1750013662297 -0.175001366229745 58 12 12.9527854746640 -0.952785474663979 59 14 12.7449908723657 1.25500912763433 60 13 14.4966154874479 -1.49661548744789 61 11 10.7488727024910 0.251127297509042 62 17 14.6086808192268 2.39131918077321 63 12 12.5653941228050 -0.565394122805016 64 13 14.095181731298 -1.09518173129801 65 14 13.8034741567030 0.196525843297026 66 13 11.3483399693084 1.65166003069163 67 15 15.872283810791 -0.872283810790993 68 13 11.1614891757878 1.83851082421219 69 10 12.2147822591059 -2.21478225910590 70 11 10.4739876615062 0.526012338493826 71 13 13.4246673852542 -0.424667385254207 72 17 13.1083644242296 3.89163557577041 73 13 14.3044777486017 -1.30447774860174 74 9 12.5540127441631 -3.55401274416306 75 11 11.0286691726578 -0.0286691726578122 76 10 14.1673078759561 -4.16730787595614 77 9 11.7221625450496 -2.72216254504961 78 12 11.9909826254313 0.0090173745686921 79 12 11.4383570098930 0.561642990106977 80 13 11.5617706880169 1.43822931198314 81 13 12.2441870177626 0.755812982237403 82 22 15.2341261634665 6.7658738365335 83 13 13.7564589424249 -0.756458942424917 84 15 14.4515729221725 0.548427077827461 85 13 11.8805329796766 1.11946702032338 86 15 12.2549764616157 2.74502353838428 87 10 13.3366595470543 -3.33665954705432 88 11 11.0866195895427 -0.0866195895426615 89 16 12.3417624273352 3.65823757266475 90 11 10.9729309681933 0.0270690318066661 91 11 10.8532606153728 0.146739384627189 92 10 11.7187604611138 -1.71876046111378 93 10 13.1497561172889 -3.14975611728886 94 16 14.2801608562786 1.71983914372139 95 12 12.4736147360917 -0.473614736091663 96 11 12.8658886504434 -1.86588865044344 97 16 13.6162823799457 2.38371762005429 98 19 14.0771807733842 4.92281922661582 99 11 13.1962560069692 -2.19625600696921 100 15 12.7336428018968 2.26635719810323 101 24 18.9116499432265 5.08835005677353 102 14 10.4785841984937 3.52141580150634 103 15 13.7943044526399 1.20569554736012 104 11 12.0725347169398 -1.07253471693977 105 15 16.2942577889932 -1.29425778899317 106 12 13.2146544509363 -1.21465445093631 107 10 9.6500868904244 0.349913109575604 108 14 13.5023660505369 0.497633949463136 109 9 12.3646848101308 -3.36468481013082 110 15 10.6494654194965 4.35053458050352 111 15 12.0340141840302 2.96598581596976 112 14 11.2387669559930 2.76123304400698 113 11 11.7340910299026 -0.734091029902645 114 8 13.5262133692316 -5.52621336923162 115 11 12.0488745326752 -1.04887453267518 116 8 9.52754882108194 -1.52754882108194 117 10 11.9813295528431 -1.98132955284310 118 11 13.3863608860011 -2.38636088600109 119 13 15.2821634934785 -2.28216349347848 120 11 13.6097524026495 -2.60975240264953 121 20 13.8144807938664 6.18551920613357 122 10 13.0354490618543 -3.03544906185427 123 12 12.1060526399449 -0.106052639944856 124 14 13.1448792563444 0.855120743655613 125 23 14.5587957637837 8.44120423621626 126 14 13.3823205423009 0.617679457699054 127 16 14.3829872693718 1.61701273062821 128 11 15.5469871772547 -4.54698717725465 129 12 13.3365112108077 -1.33651121080768 130 10 13.9195061329379 -3.91950613293792 131 14 13.9158062110682 0.0841937889317513 132 12 8.38373562514529 3.61626437485471 133 12 12.9724368034870 -0.972436803487024 134 11 10.4082253600819 0.591774639918141 135 12 13.2716892292206 -1.27168922922058 136 13 14.2867069657117 -1.28670696571170 137 17 14.1580413942186 2.8419586057814 138 9 12.2312638348988 -3.23126383489885 139 12 14.1643264871514 -2.16432648715136 140 19 13.9612577835930 5.03874221640697 141 15 14.4515729221725 0.548427077827461 142 14 13.9636266756395 0.0363733243604931 143 11 13.3863608860011 -2.38636088600109 144 9 11.1549499320304 -2.15494993203038 145 18 12.7837180609304 5.21628193906964 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.248591706793686 0.497183413587371 0.751408293206314 13 0.225164556604698 0.450329113209396 0.774835443395302 14 0.894665813520887 0.210668372958225 0.105334186479113 15 0.845911977475147 0.308176045049706 0.154088022524853 16 0.854709634633374 0.290580730733253 0.145290365366626 17 0.787688317541926 0.424623364916147 0.212311682458074 18 0.747285707458233 0.505428585083535 0.252714292541767 19 0.832689515886941 0.334620968226118 0.167310484113059 20 0.872408373218771 0.255183253562457 0.127591626781229 21 0.851548623408246 0.296902753183508 0.148451376591754 22 0.798903457135082 0.402193085729835 0.201096542864918 23 0.74674689836581 0.506506203268379 0.253253101634190 24 0.70753014119673 0.584939717606538 0.292469858803269 25 0.638934578816015 0.72213084236797 0.361065421183985 26 0.576952347908537 0.846095304182927 0.423047652091463 27 0.520940643491807 0.958118713016386 0.479059356508193 28 0.453047132852251 0.906094265704502 0.546952867147749 29 0.393865231047127 0.787730462094253 0.606134768952873 30 0.34212813110164 0.68425626220328 0.65787186889836 31 0.305852906465172 0.611705812930343 0.694147093534828 32 0.265345996633788 0.530691993267577 0.734654003366211 33 0.275806171818127 0.551612343636253 0.724193828181873 34 0.385252344931408 0.770504689862816 0.614747655068592 35 0.326083627230288 0.652167254460577 0.673916372769712 36 0.277807635048179 0.555615270096358 0.722192364951821 37 0.284354810899964 0.568709621799928 0.715645189100036 38 0.471903887288425 0.94380777457685 0.528096112711575 39 0.418557384403291 0.837114768806582 0.581442615596709 40 0.694456088798035 0.611087822403929 0.305543911201965 41 0.6652704009528 0.6694591980944 0.3347295990472 42 0.614938173018294 0.770123653963411 0.385061826981706 43 0.5638562982715 0.872287403457 0.4361437017285 44 0.507463634211452 0.985072731577096 0.492536365788548 45 0.45616552203092 0.91233104406184 0.54383447796908 46 0.411033969387926 0.822067938775853 0.588966030612074 47 0.429729533981782 0.859459067963564 0.570270466018218 48 0.57465762844508 0.85068474310984 0.42534237155492 49 0.532806981491067 0.934386037017866 0.467193018508933 50 0.54961031516914 0.90077936966172 0.45038968483086 51 0.501032994998166 0.997934010003668 0.498967005001834 52 0.454651245065642 0.909302490131285 0.545348754934358 53 0.514224361916895 0.97155127616621 0.485775638083105 54 0.517456415166409 0.965087169667183 0.482543584833591 55 0.512611951934976 0.974776096130048 0.487388048065024 56 0.666857052760711 0.666285894478578 0.333142947239289 57 0.625559767263284 0.748880465473432 0.374440232736716 58 0.579622832608901 0.840754334782197 0.420377167391099 59 0.536818567704297 0.926362864591407 0.463181432295703 60 0.530253770163985 0.93949245967203 0.469746229836015 61 0.500069540627040 0.999860918745921 0.499930459372960 62 0.502667062844050 0.994665874311899 0.497332937155950 63 0.455610620826036 0.911221241652072 0.544389379173964 64 0.411959768810811 0.823919537621623 0.588040231189189 65 0.365352935693145 0.73070587138629 0.634647064306855 66 0.336515620774442 0.673031241548884 0.663484379225558 67 0.300106809844912 0.600213619689825 0.699893190155088 68 0.283773017230553 0.567546034461107 0.716226982769447 69 0.268018297866361 0.536036595732722 0.73198170213364 70 0.230551160433883 0.461102320867766 0.769448839566117 71 0.195450524668334 0.390901049336669 0.804549475331666 72 0.239081004665451 0.478162009330903 0.760918995334549 73 0.204721404970581 0.409442809941161 0.79527859502942 74 0.218622569012311 0.437245138024622 0.781377430987689 75 0.184006872891731 0.368013745783463 0.815993127108269 76 0.229331488921802 0.458662977843604 0.770668511078198 77 0.232217092391369 0.464434184782738 0.767782907608631 78 0.201684264136215 0.40336852827243 0.798315735863785 79 0.171651694609793 0.343303389219586 0.828348305390207 80 0.149382582041517 0.298765164083034 0.850617417958483 81 0.125213525385979 0.250427050771959 0.87478647461402 82 0.287807059983279 0.575614119966559 0.71219294001672 83 0.248574728611018 0.497149457222035 0.751425271388982 84 0.210929557668204 0.421859115336408 0.789070442331796 85 0.181025526560392 0.362051053120785 0.818974473439608 86 0.180014643768129 0.360029287536258 0.819985356231871 87 0.197262494106811 0.394524988213621 0.80273750589319 88 0.165259110356421 0.330518220712841 0.83474088964358 89 0.176740799442046 0.353481598884092 0.823259200557954 90 0.148977718762997 0.297955437525994 0.851022281237003 91 0.120975021828962 0.241950043657925 0.879024978171038 92 0.106164410754285 0.212328821508569 0.893835589245715 93 0.111453666117325 0.222907332234650 0.888546333882675 94 0.0953766067019019 0.190753213403804 0.904623393298098 95 0.0775188124319832 0.155037624863966 0.922481187568017 96 0.0667945429922187 0.133589085984437 0.933205457007781 97 0.065848584123247 0.131697168246494 0.934151415876753 98 0.0971579872131819 0.194315974426364 0.902842012786818 99 0.084045123282602 0.168090246565204 0.915954876717398 100 0.0736877582513943 0.147375516502789 0.926312241748606 101 0.127215348202596 0.254430696405193 0.872784651797404 102 0.126519061023498 0.253038122046996 0.873480938976502 103 0.104414959780594 0.208829919561188 0.895585040219406 104 0.0829148322982749 0.165829664596550 0.917085167701725 105 0.065667293560023 0.131334587120046 0.934332706439977 106 0.0525276442958891 0.105055288591778 0.94747235570411 107 0.0392363028153377 0.0784726056306754 0.960763697184662 108 0.0285635317954477 0.0571270635908953 0.971436468204552 109 0.0317090667017327 0.0634181334034654 0.968290933298267 110 0.055990685471702 0.111981370943404 0.944009314528298 111 0.0475511663085757 0.0951023326171514 0.952448833691424 112 0.0444243534785634 0.0888487069571268 0.955575646521437 113 0.032187878668529 0.064375757337058 0.967812121331471 114 0.047959573087478 0.095919146174956 0.952040426912522 115 0.0351454009292341 0.0702908018584682 0.964854599070766 116 0.0281699917615394 0.0563399835230788 0.97183000823846 117 0.0277073651553392 0.0554147303106784 0.97229263484466 118 0.0219721203259303 0.0439442406518606 0.97802787967407 119 0.0168776700666443 0.0337553401332886 0.983122329933356 120 0.0194836301638318 0.0389672603276636 0.980516369836168 121 0.0474938423847729 0.0949876847695458 0.952506157615227 122 0.0599999929815366 0.119999985963073 0.940000007018463 123 0.0406902657260658 0.0813805314521315 0.959309734273934 124 0.036684833753848 0.073369667507696 0.963315166246152 125 0.432173932588721 0.864347865177442 0.567826067411279 126 0.858815959876543 0.282368080246913 0.141184040123457 127 0.884659888899689 0.230680222200622 0.115340111100311 128 0.832272355877786 0.335455288244427 0.167727644122214 129 0.78641134484149 0.427177310317019 0.213588655158509 130 0.69528464521031 0.609430709579381 0.304715354789690 131 0.743995106597645 0.51200978680471 0.256004893402355 132 0.67025088083651 0.65949823832698 0.32974911916349 133 0.835702165795158 0.328595668409683 0.164297834204842 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 3 0.0245901639344262 OK 10% type I error level 16 0.131147540983607 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/102yvc1293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/102yvc1293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/1vfyj1293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/1vfyj1293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/2vfyj1293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/2vfyj1293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/367gm1293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/367gm1293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/467gm1293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/467gm1293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/567gm1293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/567gm1293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/6hyx61293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/6hyx61293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/7a7e91293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/7a7e91293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/8a7e91293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/8a7e91293227552.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/9a7e91293227552.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293227520qiiigwaiiepm1g3/9a7e91293227552.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') }

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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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