*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: Thu, 02 Dec 2010 19:08:37 +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/02/t1291316795i70dxna35zhsokl.htm/, Retrieved Thu, 02 Dec 2010 20:06:35 +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/02/t1291316795i70dxna35zhsokl.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 «

9 13 13 14 13 3 1 1 0 9 12 12 8 13 5 1 0 0 9 15 10 12 16 6 0 0 0 9 12 9 7 12 6 2 0 1 9 10 10 10 11 5 0 1 2 9 12 12 7 12 3 0 0 1 9 15 13 16 18 8 1 1 1 9 9 12 11 11 4 1 0 0 9 12 12 14 14 4 4 0 0 9 11 6 6 9 4 0 0 0 9 11 5 16 14 6 0 2 1 9 11 12 11 12 6 2 0 0 9 15 11 16 11 5 0 2 2 9 7 14 12 12 4 1 1 1 9 11 14 7 13 6 0 1 0 9 11 12 13 11 4 0 0 1 9 10 12 11 12 6 1 1 0 9 14 11 15 16 6 2 0 1 9 10 11 7 9 4 1 0 0 9 6 7 9 11 4 1 0 0 9 11 9 7 13 2 0 1 1 9 15 11 14 15 7 1 2 0 9 11 11 15 10 5 1 2 1 9 12 12 7 11 4 2 0 0 9 14 12 15 13 6 1 0 0 9 15 11 17 16 6 1 1 0 9 9 11 15 15 7 1 1 0 9 13 8 14 14 5 2 2 0 9 13 9 14 14 6 0 0 2 9 16 12 8 14 4 1 1 1 9 13 10 8 8 4 0 1 2 9 12 10 14 13 7 1 1 1 9 14 12 14 15 7 1 2 1 9 11 8 8 13 4 0 2 0 9 9 12 11 11 4 1 1 0 9 16 11 16 15 6 2 2 0 9 12 12 10 15 6 1 1 1 9 10 7 8 9 5 1 1 2 9 13 11 14 13 6 1 0 1 9 16 11 16 16 7 1 3 1 9 14 12 13 13 6 0 1 2 9 15 9 5 11 3 1 0 0 9 5 15 8 12 3 1 0 0 9 8 11 10 12 4 1 0 0 9 11 11 8 12 6 0 1 1 9 16 11 13 14 7 2 0 1 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = -0.983357375578938 + 0.089381370655549month[t] + 0.104378264723487FindingFriends[t] + 0.211622867223575KnowingPeople[t] + 0.385193850989471Liked[t] + 0.59441013592851Celebrity[t] + 0.307649945898061bestfriend[t] -0.0324002150208811secondbestfriend[t] + 0.411552931191424thirdbestfriend[t] -0.00181007322403911t + 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) -0.983357375578938 6.011657 -0.1636 0.870292 0.435146 month 0.089381370655549 0.640842 0.1395 0.889267 0.444634 FindingFriends 0.104378264723487 0.098559 1.059 0.291327 0.145664 KnowingPeople 0.211622867223575 0.06405 3.304 0.001199 6e-04 Liked 0.385193850989471 0.099038 3.8894 0.000152 7.6e-05 Celebrity 0.59441013592851 0.156961 3.787 0.000222 0.000111 bestfriend 0.307649945898061 0.211991 1.4512 0.148859 0.074429 secondbestfriend -0.0324002150208811 0.20236 -0.1601 0.873014 0.436507 thirdbestfriend 0.411552931191424 0.214612 1.9177 0.057107 0.028553 t -0.00181007322403911 0.006864 -0.2637 0.792389 0.396194 Multiple Linear Regression - Regression Statistics Multiple R 0.719101919830674 R-squared 0.51710757110416 Adjusted R-squared 0.487340229596883 F-TEST (value) 17.3716410307495 F-TEST (DF numerator) 9 F-TEST (DF denominator) 146 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.1026328891003 Sum Squared Residuals 645.475499683636 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 11.2049026711582 1.79509732884181 2 12 11.0501976167471 0.949802383252893 3 15 13.1284642259693 1.87153577403075 4 12 11.4502389709335 0.549761029066483 5 10 10.9719246015601 -0.97192460156013 6 12 9.36122331907425 2.63877668092575 7 15 16.9268608320424 -1.92686083204242 8 9 10.3094079411661 -1.30940794116614 9 12 13.0209978602754 -1.02099786027543 10 11 7.54336622238226 3.45663377761774 11 11 13.0149485846245 -2.01494858462452 12 11 12.1838317170145 -1.18383171701454 13 15 12.2991592688119 2.70084073118813 14 7 11.4832734656525 -4.48327346565248 15 11 11.2781603020676 -0.278160302067566 16 11 10.8220760751143 0.177923924885657 17 10 11.8347311899754 -1.8347311899754 18 14 14.8674128169904 -0.867412816990424 19 10 8.56823970010499 1.43176029989501 20 6 9.34255000441309 -3.34255000441309 21 11 8.77932092658328 2.22067907341672 22 15 14.0737626346785 0.926237365321516 23 11 11.5803388330651 -0.580338833065069 24 12 9.74160524658528 2.25839475341472 25 14 13.0843361390877 0.91566386091226 26 15 14.5245748735349 0.475425126465107 27 9 14.3087353508027 -5.30873535080274 28 13 12.4834032242154 0.516596775784642 29 13 13.4529879522718 -0.452987952271803 30 16 11.1694519977055 4.83054800229448 31 13 8.75162527439104 4.24837472560896 32 12 13.6248490819480 -1.62484908194797 33 14 14.5697830251290 -0.569783025128966 34 11 9.60790170281557 1.39209829718443 35 9 10.2281357490962 -1.22813574909621 36 16 14.1849071539586 1.81509284604136 37 12 13.1540413424309 -1.15404134243088 38 10 9.71307390046834 0.286926099531656 39 13 13.1545469131956 -0.154546913195554 40 16 15.2287736182529 0.771226381747057 41 14 13.1151849345199 0.88481506548013 42 15 8.0705833181084 6.92941668189161 43 5 9.71510528588547 -4.71510528588547 44 8 10.3134380241431 -2.31343802414315 45 11 11.1487052586015 -0.148705258601458 46 16 14.2175074662197 1.78249253378025 47 17 14.2475308431785 2.75246915682148 48 9 8.0287939713907 0.971206028609305 49 9 11.3995874334900 -2.39958743349002 50 13 14.8178416598702 -1.81784165987017 51 10 10.8886453480061 -0.888645348006095 52 6 11.9910442211409 -5.99104422114088 53 12 11.8495190412859 0.150480958714061 54 8 10.4331664434410 -2.43316644344104 55 14 11.8091063908163 2.19089360918367 56 12 12.8761180336080 -0.87611803360804 57 11 11.1092335643711 -0.109233564371146 58 16 14.1747570596022 1.82524294039776 59 8 10.2776291475077 -2.27762914750774 60 15 14.6575447101163 0.342455289883668 61 7 9.1511205984398 -2.15112059843980 62 16 13.9464110345232 2.05358896547681 63 14 12.9130473900452 1.08695260995478 64 16 13.661414209331 2.33858579066899 65 9 10.2507224958308 -1.25072249583077 66 14 12.3894985830801 1.61050141691991 67 11 13.2954002601194 -2.29540026011942 68 13 10.4232974364030 2.57670256359697 69 15 13.0175810545881 1.98241894541192 70 5 5.82162779891744 -0.821627798917437 71 15 12.938348305903 2.06165169409700 72 13 11.9737875527751 1.02621244722485 73 11 13.0575037847135 -2.05750378471350 74 11 14.1346593713084 -3.13465937130842 75 12 12.2462910576544 -0.246291057654353 76 12 13.4605564914878 -1.46055649148784 77 12 12.8158509750580 -0.815850975058034 78 12 12.1256704010635 -0.125670401063511 79 14 11.0986370101107 2.90136298988933 80 6 8.02152943171598 -2.02152943171598 81 7 9.46530618764307 -2.46530618764307 82 14 12.6302867701622 1.36971322983782 83 14 14.0434433176965 -0.0434433176964578 84 10 10.9996709892448 -0.999670989244845 85 13 9.39576007708632 3.60423992291368 86 12 12.4552688010383 -0.455268801038257 87 9 9.09895259471804 -0.0989525947180393 88 12 12.3827335866523 -0.382733586652307 89 16 15.1126325964851 0.887367403514892 90 10 10.8271360363588 -0.827136036358805 91 14 13.0149414140629 0.985058585937122 92 10 13.6743590227115 -3.67435902271152 93 16 15.0189494268598 0.981050573140155 94 15 13.3269385259384 1.67306147406163 95 12 11.4956913796953 0.504308620304673 96 10 9.29698432053747 0.703015679462534 97 8 10.2975523598291 -2.29755235982908 98 8 8.4817653155485 -0.4817653155485 99 11 12.5542135160764 -1.55421351607642 100 13 13.0145764114289 -0.0145764114289043 101 16 15.8957167506989 0.104283249301134 102 16 15.1898453567617 0.810154643238305 103 14 15.6944063024031 -1.69440630240308 104 11 8.99400888584843 2.00599111415157 105 4 7.37063441742284 -3.37063441742284 106 14 14.6930170629043 -0.693017062904348 107 9 10.5659658154158 -1.56596581541582 108 14 15.2574638574311 -1.25746385743106 109 8 10.0167137756442 -2.01671377564415 110 8 10.4697500279441 -2.46975002794406 111 11 11.9652097964192 -0.965209796419187 112 12 13.1931382162864 -1.19313821628645 113 11 11.0137981823412 -0.0137981823411882 114 14 13.0197127994167 0.980287200583342 115 15 14.4011837376831 0.598816262316853 116 16 13.3534432962753 2.64655670372468 117 16 12.8731118109995 3.12688818900048 118 11 12.6472019672902 -1.64720196729016 119 14 13.3630666028829 0.636933397117079 120 14 10.9961495101169 3.00385048988307 121 12 11.5554143228783 0.444585677121667 122 14 13.0527898702698 0.94721012973019 123 8 10.8065061752171 -2.80650617521712 124 13 14.2747980236383 -1.27479802363834 125 16 14.5086598466098 1.49134015339024 126 12 10.5694280782661 1.43057192173393 127 16 15.8410718755826 0.158928124417358 128 12 12.6796300217692 -0.679630021769198 129 11 11.2682259994715 -0.268225999471477 130 4 5.69832608065274 -1.69832608065274 131 16 16.0766810985428 -0.0766810985427848 132 15 13.0638986707597 1.93610132924026 133 10 11.1235806511745 -1.12358065117449 134 13 14.2784553749570 -1.27845537495703 135 15 12.5641071957015 2.43589280429852 136 12 10.1741917457575 1.82580825424246 137 14 12.8469543039562 1.15304569604381 138 7 10.3775092422047 -3.37750924220472 139 19 13.7053566491609 5.29464335083911 140 12 13.0227660694378 -1.02276606943781 141 12 11.8821733463978 0.117826653602182 142 13 13.2070126965691 -0.20701269656909 143 15 12.4076173372964 2.59238266270357 144 8 8.9046262949945 -0.904626294994498 145 12 11.1211270460751 0.878872953924874 146 10 10.3911752139798 -0.39117521397978 147 8 11.2623122209065 -3.26231222090646 148 10 14.2774505151922 -4.27745051519218 149 15 14.1609097605415 0.839090239458519 150 16 13.9641477830056 2.03585221699442 151 13 13.1799642692192 -0.179964269219201 152 16 14.9690796300644 1.03092036993558 153 9 9.6862486002568 -0.686248600256794 154 14 13.2785335658949 0.72146643410509 155 14 13.3355686009417 0.664431399058301 156 12 9.96088127603093 2.03911872396907 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.836001792063908 0.327996415872184 0.163998207936092 14 0.90195839407854 0.196083211842921 0.0980416059214603 15 0.87924055764452 0.241518884710962 0.120759442355481 16 0.808616793673195 0.382766412653610 0.191383206326805 17 0.726585161757706 0.546829676484588 0.273414838242294 18 0.666797038131056 0.666405923737889 0.333202961868944 19 0.607910346534051 0.784179306931898 0.392089653465949 20 0.702128356781856 0.595743286436287 0.297871643218144 21 0.69419228673152 0.611615426536959 0.305807713268479 22 0.745316120165674 0.509367759668651 0.254683879834326 23 0.683418423843553 0.633163152312894 0.316581576156447 24 0.70777713203869 0.584445735922621 0.292222867961310 25 0.684535775162728 0.630928449674544 0.315464224837272 26 0.626079763990607 0.747840472018787 0.373920236009393 27 0.806167163016805 0.387665673966391 0.193832836983195 28 0.770975653320348 0.458048693359305 0.229024346679652 29 0.717035116657026 0.565929766685949 0.282964883342974 30 0.818623909680045 0.362752180639911 0.181376090319955 31 0.860156619654829 0.279686760690343 0.139843380345171 32 0.829666210992467 0.340667578015067 0.170333789007533 33 0.787312593252715 0.425374813494571 0.212687406747286 34 0.754905410599088 0.490189178801823 0.245094589400912 35 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0.0234038171161810 0.0117019085580905 70 0.985082355025686 0.0298352899486290 0.0149176449743145 71 0.985556940612976 0.0288861187740470 0.0144430593870235 72 0.982164316846268 0.0356713663074636 0.0178356831537318 73 0.982498792377714 0.035002415244572 0.017501207622286 74 0.987403010396265 0.0251939792074704 0.0125969896037352 75 0.983349535585278 0.0333009288294439 0.0166504644147220 76 0.980156242237186 0.0396875155256276 0.0198437577628138 77 0.974293116574389 0.0514137668512229 0.0257068834256115 78 0.96666173246263 0.0666765350747412 0.0333382675373706 79 0.975640128211654 0.0487197435766924 0.0243598717883462 80 0.9739942985895 0.0520114028209996 0.0260057014104998 81 0.974966944190363 0.0500661116192737 0.0250330558096368 82 0.972983482915995 0.0540330341680097 0.0270165170840049 83 0.964448945316955 0.0711021093660909 0.0355510546830455 84 0.955210581156972 0.0895788376860567 0.0447894188430283 85 0.981998073761221 0.0360038524775577 0.0180019262387789 86 0.976014075078278 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0.191820133227881 104 0.864435874821128 0.271128250357745 0.135564125178872 105 0.861859391898908 0.276281216202183 0.138140608101092 106 0.829887256031703 0.340225487936594 0.170112743968297 107 0.797467852063354 0.405064295873292 0.202532147936646 108 0.783656372757127 0.432687254485746 0.216343627242873 109 0.764854625535922 0.470290748928157 0.235145374464078 110 0.784143309068944 0.431713381862112 0.215856690931056 111 0.769045385873406 0.461909228253187 0.230954614126594 112 0.834324256100617 0.331351487798766 0.165675743899383 113 0.797250203792304 0.405499592415392 0.202749796207696 114 0.766332162739474 0.467335674521053 0.233667837260526 115 0.722976130244068 0.554047739511864 0.277023869755932 116 0.732170131757851 0.535659736484297 0.267829868242149 117 0.74437482273268 0.511250354534641 0.255625177267320 118 0.725622971067391 0.548754057865217 0.274377028932609 119 0.675379918339688 0.649240163320624 0.324620081660312 120 0.771318547410679 0.457362905178642 0.228681452589321 121 0.740958354445508 0.518083291108984 0.259041645554492 122 0.688252418566368 0.623495162867264 0.311747581433632 123 0.674328362110953 0.651343275778094 0.325671637889047 124 0.631483536872456 0.737032926255088 0.368516463127544 125 0.612176877947888 0.775646244104225 0.387823122052112 126 0.612552819280794 0.774894361438412 0.387447180719206 127 0.54603567075918 0.90792865848164 0.45396432924082 128 0.504878729170788 0.990242541658424 0.495121270829212 129 0.482994137738117 0.965988275476233 0.517005862261883 130 0.465009250028874 0.930018500057748 0.534990749971126 131 0.392923101575466 0.785846203150931 0.607076898424534 132 0.371462252534148 0.742924505068295 0.628537747465852 133 0.325667334331613 0.651334668663227 0.674332665668387 134 0.281323824371726 0.562647648743452 0.718676175628274 135 0.237546151569607 0.475092303139213 0.762453848430393 136 0.219069151340993 0.438138302681986 0.780930848659007 137 0.18430668893371 0.36861337786742 0.81569331106629 138 0.197580908202360 0.395161816404721 0.80241909179764 139 0.616069745068915 0.76786050986217 0.383930254931085 140 0.526150517910437 0.947698964179126 0.473849482089563 141 0.405569862606173 0.811139725212346 0.594430137393827 142 0.400851681624012 0.801703363248024 0.599148318375988 143 0.278089005617193 0.556178011234386 0.721910994382807 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 29 0.221374045801527 NOK 10% type I error level 48 0.366412213740458 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/107kt71291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/107kt71291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/1jjwd1291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/1jjwd1291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/2tswy1291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/2tswy1291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/3tswy1291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/3tswy1291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/4mkd11291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/4mkd11291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/5mkd11291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/5mkd11291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/6mkd11291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/6mkd11291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/7xbum1291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/7xbum1291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/8xbum1291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/8xbum1291316906.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/97kt71291316906.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291316795i70dxna35zhsokl/97kt71291316906.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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

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

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Software written by Ed van Stee & Patrick Wessa

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