*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: Tue, 14 Dec 2010 13:29:25 +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/14/t1292333252epn2up3y5buzqsr.htm/, Retrieved Tue, 14 Dec 2010 14:27:35 +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/14/t1292333252epn2up3y5buzqsr.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 24 14 11 12 24 26 0 25 11 7 8 25 23 0 17 6 17 8 30 25 1 18 12 10 8 19 23 1 18 8 12 9 22 19 1 16 10 12 7 22 29 1 20 10 11 4 25 25 1 16 11 11 11 23 21 1 18 16 12 7 17 22 1 17 11 13 7 21 25 0 23 13 14 12 19 24 0 30 12 16 10 19 18 1 23 8 11 10 15 22 1 18 12 10 8 16 15 1 15 11 11 8 23 22 1 12 4 15 4 27 28 0 21 9 9 9 22 20 1 15 8 11 8 14 12 1 20 8 17 7 22 24 0 31 14 17 11 23 20 0 27 15 11 9 23 21 1 34 16 18 11 21 20 1 21 9 14 13 19 21 1 31 14 10 8 18 23 1 19 11 11 8 20 28 0 16 8 15 9 23 24 1 20 9 15 6 25 24 1 21 9 13 9 19 24 1 22 9 16 9 24 23 1 17 9 13 6 22 23 1 24 10 9 6 25 29 0 25 16 18 16 26 24 0 26 11 18 5 29 18 1 25 8 12 7 32 25 1 17 9 17 9 25 21 1 32 16 9 6 29 26 1 33 11 9 6 28 22 1 13 16 12 5 17 22 1 32 12 18 12 28 22 1 25 12 12 7 29 23 1 29 14 18 10 26 30 1 22 9 14 9 25 23 1 18 10 15 8 14 17 1 17 9 16 5 25 23 0 20 10 10 8 26 23 1 15 12 11 8 20 25 1 20 14 14 10 18 24 1 33 14 9 6 32 24 0 29 10 12 8 25 23 1 23 14 17 7 25 21 0 26 16 5 4 23 24 1 18 9 12 8 21 2 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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Concern_Mistakes[t] = -1.5248892301341 -0.599776425471945Gender[t] + 0.812146509740824Doubts_Actions[t] + 0.258421861231516Par_Exp[t] + 0.179796229544163Par_Crit[t] + 0.563133007199381Personal_Standards[t] -0.115118381547464Organisation[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) -1.5248892301341 3.115376 -0.4895 0.625213 0.312607 Gender -0.599776425471945 0.803715 -0.7463 0.456666 0.228333 Doubts_Actions 0.812146509740824 0.130552 6.2209 0 0 Par_Exp 0.258421861231516 0.133299 1.9387 0.054395 0.027198 Par_Crit 0.179796229544163 0.168908 1.0645 0.288806 0.144403 Personal_Standards 0.563133007199381 0.096033 5.864 0 0 Organisation -0.115118381547464 0.103177 -1.1157 0.266294 0.133147 Multiple Linear Regression - Regression Statistics Multiple R 0.639796340883729 R-squared 0.409339357808208 Adjusted R-squared 0.386023806142743 F-TEST (value) 17.5564946384912 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 2.22044604925031e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.48420824730765 Sum Squared Residuals 3056.43478799373 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 25.3674713868651 -1.36747138686511 2 25 22.0866476463817 2.91335235361826 3 17 23.1955619828948 -6.19556198289476 4 18 19.6954852711489 -1.69548527114888 5 18 19.2934117319808 -1.29341173198078 6 16 19.4069284768995 -3.40692847689946 7 20 20.7589904748235 -0.758990474823453 8 16 22.1639181031645 -6.16391810316451 9 18 22.2699711701797 -4.26997117017974 10 17 20.3748373668623 -3.37483736686227 11 23 22.7451621879169 0.2548378120831 12 30 22.7809772308356 7.21902276916444 13 23 14.9274999052554 8.07250009474463 14 18 18.9270333019304 -0.927033301930445 15 15 21.5094110329846 -6.50941103298456 16 12 17.7007097310609 -5.70070973106094 17 21 19.8149507019515 1.18504929804847 18 15 15.1559582544423 -0.155958254442296 19 20 19.6503366713127 0.349663328687287 20 31 26.8657836067955 4.13421639320451 21 27 25.6526881085114 1.34731189148858 22 34 27.0224560476379 6.97754395236206 23 21 19.4219510976682 1.57804890233179 24 31 20.7566452834311 10.2433547165689 25 19 19.1293017221016 -0.129301722101632 26 16 20.6559948406093 -4.65599484060933 27 20 21.4552422506445 -1.45524225064448 28 21 18.0989891736177 2.90101082638235 29 22 21.8050381748566 0.194961825143429 30 17 19.3641178881308 -2.36411788813077 31 24 20.1412656852589 3.85873431474111 32 25 30.8764051306378 -5.87640513063775 33 26 27.2180233678308 -1.21802336783077 34 25 23.8744390556015 1.12556094439852 35 17 22.8568298063824 -5.8568298063824 36 32 27.6120319171437 4.38796808285625 37 33 23.4486398874301 9.5513601125699 38 13 21.9103787110914 -8.91037871109142 39 32 27.6653605255195 4.33463947448045 40 25 25.6638628360616 -0.663862836061556 41 29 26.8828480191344 2.1171519808656 42 22 21.8513274595929 0.148672540407081 43 18 17.2383468111127 0.76165318888731 44 17 21.6489862638793 -4.6489862638793 45 20 22.6128997275348 -2.61289972753484 46 15 20.2868033764848 -5.28680337648485 47 20 22.0348068058981 -2.03480680589807 48 33 27.9073746823552 5.09262531764483 49 29 22.5666104427985 6.43338955720151 50 23 26.5579698959982 -3.55796989599819 51 26 23.6699671584999 2.33003284150006 52 18 18.7870370972407 -0.787037097240737 53 20 19.1753534990643 0.82464650093573 54 11 11.6230304916359 -0.62303049163594 55 28 28.7621782677923 -0.762178267792346 56 26 23.1494302500649 2.85056974993514 57 22 22.669269923808 -0.669269923808019 58 17 20.0517776355677 -3.05177763556769 59 12 15.8538857599063 -3.85388575990633 60 14 20.8973540189012 -6.89735401890124 61 17 20.5411139668356 -3.54111396683558 62 21 21.1874120307229 -0.187412030722894 63 19 22.7894496147904 -3.78944961479037 64 18 22.8711309424203 -4.87113094242027 65 10 18.3900018497382 -8.3900018497382 66 29 24.6370720525763 4.36292794742368 67 31 18.2796017334678 12.7203982665322 68 19 23.3693885717829 -4.36938857178287 69 9 19.9178025587064 -10.9178025587064 70 20 22.2903219027409 -2.29032190274093 71 28 17.4621981138513 10.5378018861487 72 19 18.5468700910214 0.453129908978567 73 30 23.4904366160527 6.50956338394725 74 29 27.5909968331254 1.40900316687456 75 26 21.939900294569 4.06009970543104 76 23 19.9811821698965 3.01881783010348 77 13 22.6314806665406 -9.63148066654058 78 21 22.4832495856698 -1.48324958566978 79 19 21.2573070106616 -2.25730701066162 80 28 22.792928310758 5.20707168924198 81 23 25.3729608504198 -2.37296085041983 82 18 13.6094069148943 4.3905930851057 83 21 21.1856456618628 -0.185645661862836 84 20 21.6207025203118 -1.62070252031183 85 23 19.8030723533452 3.19692764665485 86 21 20.7020883249049 0.297911675095094 87 21 21.6170380931145 -0.617038093114459 88 15 22.6231781689117 -7.62317816891175 89 28 26.9449762549455 1.05502374505454 90 19 17.5122104932823 1.48778950671773 91 26 21.1120813396104 4.88791866038963 92 10 13.0189051398643 -3.01890513986429 93 16 17.5590386226172 -1.55903862261723 94 22 21.038463264977 0.96153673502298 95 19 18.5428525504552 0.457147449544845 96 31 28.6083915923401 2.39160840765988 97 31 25.6533606613582 5.34663933864182 98 29 24.6506943664122 4.34930563358783 99 19 17.8629734162128 1.13702658378723 100 22 18.7437983543523 3.25620164564775 101 23 22.2056628088056 0.794337191194412 102 15 16.6220171743863 -1.62201717438634 103 20 21.913173055602 -1.91317305560195 104 18 19.3399853933777 -1.33998539337771 105 23 21.810215328865 1.18978467113503 106 25 20.8334925012105 4.1665074987895 107 21 16.3946802847268 4.60531971527318 108 24 19.314850837089 4.68514916291101 109 25 25.0709120701137 -0.0709120701136657 110 17 19.4547342883026 -2.45473428830264 111 13 14.4287552152974 -1.4287552152974 112 28 18.0383639290045 9.9616360709955 113 21 20.4886626656978 0.511337334302206 114 25 27.9423547054043 -2.94235470540425 115 9 21.0762576763391 -12.0762576763391 116 16 17.6939237600989 -1.69392376009894 117 19 21.046422718448 -2.04642271844801 118 17 19.2889309744774 -2.28893097447745 119 25 24.3659644663829 0.634035533617051 120 20 15.4196958930467 4.58030410695328 121 29 21.44593769148 7.55406230852002 122 14 18.8999489107604 -4.89994891076041 123 22 26.7321259935752 -4.73212599357522 124 15 15.3865155722756 -0.386515572275644 125 19 25.8655570594453 -6.86555705944529 126 20 21.8185578044274 -1.81855780442743 127 15 17.9017713795316 -2.90177137953155 128 20 21.6120705139941 -1.61207051399408 129 18 20.0655867958938 -2.06558679589383 130 33 25.2898944391628 7.71010556083722 131 22 23.5892499774693 -1.58924997746931 132 16 16.3167272058862 -0.316727205886224 133 17 18.8852557986806 -1.88525579868057 134 16 14.906159445578 1.09384055442197 135 21 17.4984133780041 3.50158662199593 136 26 28.1082687097083 -2.10826870970826 137 18 21.0102381889068 -3.0102381889068 138 18 22.927996914261 -4.92799691426099 139 17 18.1546868170441 -1.15468681704408 140 22 24.5338027250514 -2.5338027250514 141 30 24.5297501216679 5.47024987833213 142 30 27.693411915408 2.306588084592 143 24 29.7180440769175 -5.7180440769175 144 21 21.8284181608956 -0.82841816089561 145 21 25.2236239084878 -4.22362390848779 146 29 27.2328463883029 1.76715361169705 147 31 23.0870665694773 7.91293343052268 148 20 18.8092925326549 1.19070746734509 149 16 14.6384488700858 1.36155112991424 150 22 19.3484437135137 2.65155628648631 151 20 20.1300202451636 -0.130020245163566 152 28 27.1951491319507 0.804850868049289 153 38 26.4868027881196 11.5131972118804 154 22 19.7992193502582 2.20078064974181 155 20 25.5275587020833 -5.52755870208333 156 17 18.5047320550995 -1.5047320550995 157 28 24.2787468063115 3.72125319368852 158 22 23.9638509492812 -1.96385094928122 159 31 26.5271034532647 4.4728965467353 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0844420500201226 0.168884100040245 0.915557949979877 11 0.0464752780920931 0.0929505561841861 0.953524721907907 12 0.130278876290243 0.260557752580486 0.869721123709757 13 0.0844961437839683 0.168992287567937 0.915503856216032 14 0.129771754078389 0.259543508156779 0.87022824592161 15 0.0837902520070481 0.167580504014096 0.916209747992952 16 0.061635540514751 0.123271081029502 0.93836445948525 17 0.0823406090997286 0.164681218199457 0.917659390900271 18 0.103431029533643 0.206862059067287 0.896568970466357 19 0.098951589691939 0.197903179383878 0.901048410308061 20 0.135233178248043 0.270466356496086 0.864766821751957 21 0.0964672042442725 0.192934408488545 0.903532795755728 22 0.27377445665778 0.54754891331556 0.72622554334222 23 0.213650645233384 0.427301290466768 0.786349354766616 24 0.517651166481889 0.964697667036223 0.482348833518111 25 0.44430888469239 0.888617769384779 0.55569111530761 26 0.471727515677312 0.943455031354624 0.528272484322688 27 0.415577906259593 0.831155812519187 0.584422093740407 28 0.372566640640189 0.745133281280378 0.627433359359811 29 0.321730152490764 0.643460304981528 0.678269847509236 30 0.268755298152277 0.537510596304554 0.731244701847723 31 0.348260396122866 0.696520792245733 0.651739603877134 32 0.390621718429582 0.781243436859165 0.609378281570418 33 0.339860064439077 0.679720128878154 0.660139935560923 34 0.394515220214514 0.789030440429027 0.605484779785486 35 0.392960755307926 0.785921510615851 0.607039244692074 36 0.399333364164459 0.798666728328917 0.600666635835541 37 0.592350983294008 0.815298033411985 0.407649016705992 38 0.785550313920866 0.428899372158268 0.214449686079134 39 0.780877336142334 0.438245327715332 0.219122663857666 40 0.74129423560946 0.51741152878108 0.25870576439054 41 0.715508356100145 0.568983287799709 0.284491643899855 42 0.667162566875003 0.665674866249995 0.332837433124998 43 0.619217034978155 0.76156593004369 0.380782965021845 44 0.604204389006928 0.791591221986145 0.395795610993072 45 0.567088000813683 0.865823998372635 0.432911999186317 46 0.588424062937882 0.823151874124236 0.411575937062118 47 0.547730827594952 0.904538344810096 0.452269172405048 48 0.535401163172005 0.92919767365599 0.464598836827995 49 0.601861514362917 0.796276971274166 0.398138485637083 50 0.580082643413099 0.839834713173802 0.419917356586901 51 0.542717277929412 0.914565444141176 0.457282722070588 52 0.492846170622925 0.98569234124585 0.507153829377075 53 0.452222662546296 0.904445325092591 0.547777337453704 54 0.404516829329889 0.809033658659778 0.595483170670111 55 0.359411189714611 0.718822379429221 0.640588810285389 56 0.332533802571894 0.665067605143788 0.667466197428106 57 0.288601229177288 0.577202458354575 0.711398770822712 58 0.263304610154698 0.526609220309397 0.736695389845302 59 0.244943182346779 0.489886364693558 0.755056817653221 60 0.303065244327151 0.606130488654302 0.696934755672849 61 0.289369447181478 0.578738894362956 0.710630552818522 62 0.251115929215076 0.502231858430153 0.748884070784924 63 0.237127944220827 0.474255888441655 0.762872055779173 64 0.266326862706789 0.532653725413578 0.733673137293211 65 0.351174242752546 0.702348485505092 0.648825757247454 66 0.349383910768305 0.69876782153661 0.650616089231695 67 0.68358034050748 0.632839318985039 0.316419659492519 68 0.678779630231637 0.642440739536726 0.321220369768363 69 0.849018383732479 0.301963232535042 0.150981616267521 70 0.829029595089471 0.341940809821057 0.170970404910529 71 0.933488282012463 0.133023435975075 0.0665117179875373 72 0.917208616935737 0.165582766128526 0.0827913830642631 73 0.937387932560046 0.125224134879907 0.0626120674399535 74 0.924638154689406 0.150723690621188 0.0753618453105941 75 0.924019499881152 0.151961000237696 0.0759805001188479 76 0.91589199642776 0.168216007144479 0.0841080035722397 77 0.967098379570535 0.0658032408589303 0.0329016204294652 78 0.959048374082739 0.0819032518345226 0.0409516259172613 79 0.950657267973882 0.098685464052237 0.0493427320261185 80 0.954160566639851 0.0916788667202977 0.0458394333601489 81 0.945461035479997 0.109077929040007 0.0545389645200033 82 0.945131042219938 0.109737915560123 0.0548689577800616 83 0.930773668713893 0.138452662572214 0.069226331286107 84 0.916628973684818 0.166742052630363 0.0833710263151816 85 0.907956640746761 0.184086718506477 0.0920433592532386 86 0.891859186466487 0.216281627067027 0.108140813533513 87 0.869410974360294 0.261178051279413 0.130589025639706 88 0.914884723383783 0.170230553232434 0.0851152766162169 89 0.89716090666486 0.20567818667028 0.10283909333514 90 0.876485606412921 0.247028787174159 0.12351439358708 91 0.878605257035801 0.242789485928398 0.121394742964199 92 0.866875768263779 0.266248463472442 0.133124231736221 93 0.843596113524808 0.312807772950384 0.156403886475192 94 0.81547333487834 0.36905333024332 0.18452666512166 95 0.781491494107807 0.437017011784387 0.218508505892193 96 0.752079323785577 0.495841352428846 0.247920676214423 97 0.770068792292651 0.459862415414698 0.229931207707349 98 0.764369298000278 0.471261403999443 0.235630701999722 99 0.727740692197548 0.544518615604904 0.272259307802452 100 0.702831681335258 0.594336637329485 0.297168318664742 101 0.659377625238887 0.681244749522225 0.340622374761113 102 0.61957469148144 0.76085061703712 0.38042530851856 103 0.580960151237174 0.838079697525653 0.419039848762826 104 0.537989483789911 0.924021032420178 0.462010516210089 105 0.491796554578656 0.983593109157312 0.508203445421344 106 0.473454294885587 0.946908589771175 0.526545705114413 107 0.470712762093607 0.941425524187215 0.529287237906393 108 0.482821948087738 0.965643896175477 0.517178051912262 109 0.432307389908603 0.864614779817206 0.567692610091397 110 0.408228619747199 0.816457239494399 0.591771380252801 111 0.368641982308945 0.737283964617889 0.631358017691055 112 0.5969255547499 0.8061488905002 0.4030744452501 113 0.58411803181028 0.83176393637944 0.41588196818972 114 0.552622643035536 0.894754713928929 0.447377356964464 115 0.79288271461248 0.414234570775039 0.20711728538752 116 0.76597364198466 0.468052716030679 0.234026358015339 117 0.751625117907193 0.496749764185615 0.248374882092807 118 0.720775263703318 0.558449472593365 0.279224736296682 119 0.673303551854724 0.653392896290551 0.326696448145276 120 0.702407113800498 0.595185772399004 0.297592886199502 121 0.754183274449282 0.491633451101437 0.245816725550718 122 0.744794612473908 0.510410775052184 0.255205387526092 123 0.74670470193312 0.506590596133761 0.253295298066881 124 0.695736418514833 0.608527162970334 0.304263581485167 125 0.78535276127588 0.429294477448239 0.214647238724119 126 0.747396829293261 0.505206341413478 0.252603170706739 127 0.786170926338392 0.427658147323216 0.213829073661608 128 0.756420398622096 0.487159202755808 0.243579601377904 129 0.721057190714839 0.557885618570322 0.278942809285161 130 0.781273045377802 0.437453909244396 0.218726954622198 131 0.73060127775828 0.538797444483441 0.269398722241721 132 0.670855513792557 0.658288972414886 0.329144486207443 133 0.653577624333845 0.692844751332309 0.346422375666155 134 0.585458935116987 0.829082129766027 0.414541064883014 135 0.528290390709153 0.943419218581695 0.471709609290847 136 0.58570911619508 0.82858176760984 0.41429088380492 137 0.550168402151443 0.899663195697114 0.449831597848557 138 0.554961084126531 0.890077831746937 0.445038915873469 139 0.474402977270854 0.948805954541708 0.525597022729146 140 0.394560770488017 0.789121540976033 0.605439229511983 141 0.540144050850721 0.919711898298558 0.459855949149279 142 0.459016322924555 0.91803264584911 0.540983677075445 143 0.376580101360571 0.753160202721141 0.623419898639429 144 0.286752679566945 0.57350535913389 0.713247320433055 145 0.30398419702743 0.60796839405486 0.69601580297257 146 0.214007842756545 0.428015685513091 0.785992157243455 147 0.329429442876014 0.658858885752029 0.670570557123986 148 0.234700554394278 0.469401108788556 0.765299445605722 149 0.609356759640064 0.781286480719872 0.390643240359936 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 5 0.0357142857142857 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/10ktpq1292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/10ktpq1292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/1dr9e1292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/1dr9e1292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/2o1901292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/2o1901292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/3o1901292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/3o1901292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/4o1901292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/4o1901292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/5hsql1292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/5hsql1292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/6hsql1292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/6hsql1292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/79jpn1292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/79jpn1292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/89jpn1292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/89jpn1292333353.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/9ktpq1292333353.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292333252epn2up3y5buzqsr/9ktpq1292333353.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by