workshop 8 - 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: Mon, 29 Nov 2010 17:42:26 +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/Nov/29/t1291052666yuh45sv0x0mbuvg.htm/, Retrieved Mon, 29 Nov 2010 18:44:36 +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/Nov/29/t1291052666yuh45sv0x0mbuvg.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 «

24 14 11 12 24 26 25 11 7 8 25 23 17 6 17 8 30 25 18 12 10 8 19 23 18 8 12 9 22 19 16 10 12 7 22 29 20 10 11 4 25 25 16 11 11 11 23 21 18 16 12 7 17 22 17 11 13 7 21 25 23 13 14 12 19 24 30 12 16 10 19 18 23 8 11 10 15 22 18 12 10 8 16 15 15 11 11 8 23 22 12 4 15 4 27 28 21 9 9 9 22 20 15 8 11 8 14 12 20 8 17 7 22 24 31 14 17 11 23 20 27 15 11 9 23 21 34 16 18 11 21 20 21 9 14 13 19 21 31 14 10 8 18 23 19 11 11 8 20 28 16 8 15 9 23 24 20 9 15 6 25 24 21 9 13 9 19 24 22 9 16 9 24 23 17 9 13 6 22 23 24 10 9 6 25 29 25 16 18 16 26 24 26 11 18 5 29 18 25 8 12 7 32 25 17 9 17 9 25 21 32 16 9 6 29 26 33 11 9 6 28 22 13 16 12 5 17 22 32 12 18 12 28 22 25 12 12 7 29 23 29 14 18 10 26 30 22 9 14 9 25 23 18 10 15 8 14 17 17 9 16 5 25 23 20 10 10 8 26 23 15 12 11 8 20 25 20 14 14 10 18 24 33 14 9 6 32 24 29 10 12 8 25 23 23 14 17 7 25 21 26 16 5 4 23 24 18 9 12 8 21 24 20 10 12 8 20 28 11 6 6 4 15 16 28 8 24 20 30 20 26 13 12 8 24 29 22 10 12 8 26 27 17 8 14 6 24 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 Personal-Standards[t] = + 6.85249471188449 + 0.334156412849869`Concern(Mistakes)`[t] -0.370092394686342`Doubts(actions)`[t] + 0.159316471007100`Parental-Expectations`[t] + 0.0649584207606242`Parental-Criticism`[t] + 0.40340194756281Organization[t] -0.112988112395837M1[t] + 0.301657650441828M2[t] + 0.663745180491901M3[t] + 0.151960848781341M4[t] + 0.372819591437056M5[t] + 1.02662475283370M6[t] + 0.417404861828108M7[t] + 1.66380986838678M8[t] + 1.41001962150359M9[t] + 0.924259509685394M10[t] -0.535671626926259M11[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) 6.85249471188449 2.478774 2.7645 0.006459 0.003229 `Concern(Mistakes)` 0.334156412849869 0.059692 5.598 0 0 `Doubts(actions)` -0.370092394686342 0.116287 -3.1826 0.001793 0.000897 `Parental-Expectations` 0.159316471007100 0.106628 1.4941 0.137359 0.068679 `Parental-Criticism` 0.0649584207606242 0.139434 0.4659 0.642021 0.32101 Organization 0.40340194756281 0.076139 5.2982 0 0 M1 -0.112988112395837 1.367893 -0.0826 0.934286 0.467143 M2 0.301657650441828 1.369878 0.2202 0.826026 0.413013 M3 0.663745180491901 1.362393 0.4872 0.626874 0.313437 M4 0.151960848781341 1.404563 0.1082 0.913997 0.456999 M5 0.372819591437056 1.399561 0.2664 0.790331 0.395165 M6 1.02662475283370 1.401881 0.7323 0.465181 0.23259 M7 0.417404861828108 1.423488 0.2932 0.769777 0.384888 M8 1.66380986838678 1.397453 1.1906 0.235797 0.117898 M9 1.41001962150359 1.382136 1.0202 0.309381 0.15469 M10 0.924259509685394 1.371739 0.6738 0.501542 0.250771 M11 -0.535671626926259 1.422233 -0.3766 0.707002 0.353501 Multiple Linear Regression - Regression Statistics Multiple R 0.622661478001645 R-squared 0.387707316187193 Adjusted R-squared 0.318716591250539 F-TEST (value) 5.61970201854202 F-TEST (DF numerator) 16 F-TEST (DF denominator) 142 p-value 3.06102809766173e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.48067319339608 Sum Squared Residuals 1720.34219485010 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 22.5983998491152 1.40160015088476 2 25 22.3501737991026 2.64982620089742 3 30 24.2894406049820 5.71055939501796 4 19 19.9692391258280 -0.969239125827973 5 22 20.4404510197526 1.55954898024735 6 22 23.5898612001837 -1.58986120018373 7 25 22.3494674370374 2.65053256296262 8 23 20.7302555525834 2.26974444741663 9 17 19.5972008934956 -2.59720089349562 10 21 21.9972686559548 -0.997268655954795 11 19 21.8827978343171 -2.88279783431708 12 19 22.8959611609948 -3.89596116099485 13 15 22.7412731726110 -7.74127317261105 14 16 16.8917203469860 -0.891720346985974 15 23 19.6045611371196 3.39543886288044 16 27 23.4787982160266 3.52120178397345 17 22 20.9982803981574 1.00171960184259 18 14 17.0436984178923 -3.04369841789228 19 22 23.8370243671717 -1.83702436717173 20 23 25.1848214397522 -2.18482143975215 21 23 22.5418994267821 0.458100573217885 22 21 24.8668720012348 -3.86687200123479 23 19 21.5496071654349 -2.54960716543491 24 18 23.4211268547222 -5.42112685472224 25 20 22.5848651810082 -2.58486518100816 26 23 22.1959354038930 0.804064596106974 27 25 23.3296809283744 1.67031907162564 28 19 23.0282953297813 -4.02829532978134 29 24 23.6578579507454 0.342142049254590 30 22 21.9680563725895 0.0319436274104627 31 25 25.1109847781951 -0.110984778195149 32 26 24.5374145383417 1.46258546165828 33 29 23.3332883639964 5.66671163600362 34 32 25.6214806718057 6.37851932819433 35 25 20.4310972440142 4.56890275598577 36 29 23.9360710083595 5.0639289916405 37 28 24.394093491994 3.60590650800599 38 17 16.6881400166633 0.311859983336738 39 28 26.2901767409732 1.70982325902682 40 29 22.5620085370306 6.43799146296938 41 26 27.3538958629773 -1.35389586297727 42 25 23.9930301701279 1.00696982987215 43 14 19.3510385979061 -5.35103859790606 44 25 23.0182324804033 1.98176751959671 45 26 22.6357955136226 3.36420448637735 46 20 20.7051889143151 -0.70518891431514 47 18 20.3803193595599 -2.38031935955988 48 32 24.2036083154564 7.79639168454357 49 25 24.4388284373862 0.561171562613768 50 25 21.2929861835286 3.70701381647143 51 23 21.0208910910769 1.97910890892308 52 21 21.801551199464 -0.801551199464008 53 20 23.9342381633844 -3.93423816338436 54 15 17.0044493080387 -2.00444930803873 55 30 26.8563426466573 3.14365735334275 56 24 26.5232916809371 -2.52329168093708 57 26 25.2363490715878 0.763650928412177 58 24 21.9916980475719 2.00830195242813 59 22 20.4063567882031 1.59364321179687 60 14 15.0245112216698 -1.02451122166975 61 24 21.6708061937604 2.3291938062396 62 24 22.6130244744736 1.38697552552639 63 24 23.4044970714702 0.595502928529774 64 24 19.7488741574509 4.25112584254914 65 19 18.2214725637826 0.778527436217351 66 31 27.3593378655659 3.64066213443408 67 22 26.6218045032383 -4.62180450323831 68 27 22.5966672602380 4.40333273976202 69 19 18.5266848418194 0.473315158180565 70 25 22.7460633732508 2.25393662674917 71 20 23.9538129310552 -3.95381293105519 72 21 21.0374369821878 -0.0374369821878016 73 27 26.8961909213928 0.103809078607203 74 23 24.1537863151167 -1.15378631511666 75 25 25.7380213530364 -0.738021353036376 76 20 21.8920350263386 -1.89203502633858 77 21 18.9339674814873 2.06603251851267 78 22 22.9519985207818 -0.951998520781768 79 23 22.9892512371015 0.0107487628985287 80 25 25.1373752925557 -0.137375292555674 81 25 24.3793692182207 0.62063078177933 82 17 24.3316144685456 -7.3316144685456 83 19 20.3586233541285 -1.35862335412847 84 25 23.4551782966184 1.54482170338161 85 19 21.7349870254826 -2.73498702548262 86 20 22.7858399135692 -2.78583991356919 87 26 22.5915206417489 3.40847935825114 88 23 20.4876803122754 2.51231968772456 89 27 24.4739094000259 2.52609059997409 90 17 21.3651148582308 -4.36511485823084 91 17 23.2084174838922 -6.20841748389221 92 19 21.3533357694662 -2.35333576946622 93 17 20.5748857342479 -3.57488573424786 94 22 22.27480789833 -0.274807898330001 95 21 22.6288813498089 -1.62888134980886 96 32 28.1240171402766 3.87598285972337 97 21 24.0769821528684 -3.07698215286836 98 21 24.1521256301243 -3.15212563012434 99 18 21.3723276805754 -3.37232768057538 100 18 20.9103911893702 -2.91039118937019 101 23 22.7207600235368 0.279239976463159 102 19 21.0738079701298 -2.07380797012977 103 20 20.7532245962790 -0.753224596278977 104 21 23.4845549731727 -2.48455497317272 105 20 24.8675401713321 -4.86754017133209 106 17 19.0776735155312 -2.07767351553116 107 18 19.2790753612779 -1.27907536127786 108 19 20.3131446122449 -1.31314461224493 109 22 21.4878539874189 0.512146012581061 110 15 18.4265069306332 -3.42650693063324 111 14 18.9306777030369 -4.93067770303689 112 18 26.4103072897199 -8.41030728971993 113 24 21.498289634365 2.50171036563499 114 35 24.0824552382229 10.9175447617771 115 29 19.3240721116116 9.67592788838835 116 21 22.9796209967813 -1.97962099678127 117 25 21.2666064485025 3.73339355149745 118 20 18.8746326657889 1.12536733421109 119 22 22.1454594740557 -0.145459474055698 120 13 16.2431973967479 -3.24319739674791 121 26 22.6089180570591 3.39108194294092 122 17 16.5822635590892 0.41773644091077 123 25 20.2959976756244 4.70400232437565 124 20 20.4867606355997 -0.486760635599709 125 19 17.9867276561936 1.01327234380639 126 21 22.9975235473161 -1.99752354731611 127 22 20.8561517435908 1.14384825640918 128 24 23.889015679515 0.110984320484983 129 21 23.8799128354004 -2.87991283540036 130 26 25.9798195562401 0.0201804437599116 131 24 19.6441396336953 4.35586036630469 132 16 19.7710716822945 -3.7710716822945 133 23 21.6550579872736 1.34494201272639 134 18 20.5118425264845 -2.51184252648450 135 16 22.542310427475 -6.54231042747502 136 26 23.59207763797 2.40792236202999 137 19 18.7985157316019 0.201484268398138 138 21 17.3527913880341 3.64720861196585 139 21 22.1678896151079 -1.16788961510788 140 22 19.8638308674614 2.13616913253862 141 23 20.7720144850642 2.22798551493581 142 29 25.2745119899244 3.72548801007562 143 21 18.3120666627572 2.68793333724275 144 21 19.4400476037968 1.55995239620315 145 23 21.2433556202828 1.75664437971718 146 27 22.7470884671403 4.25291153285969 147 25 25.5018999362049 -0.501899936204929 148 21 20.6319813431448 0.368018656855221 149 10 16.9816341139897 -6.9816341139897 150 20 23.2178751428864 -3.21787514288639 151 26 22.5743308822111 3.42566911778890 152 24 24.7015834687921 -0.70158346879213 153 29 32.3884529959283 -3.38845299592827 154 19 19.2583682415068 -0.258368241506761 155 24 21.0277628416921 2.97223715830786 156 19 20.1346277246302 -1.13462772463022 157 24 22.8683879223467 1.13161207765330 158 22 21.6085664331955 0.391433566804472 159 17 24.0879970083019 -7.08799700830192 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.757430088246458 0.485139823507084 0.242569911753542 21 0.701305383097989 0.597389233804021 0.298694616902011 22 0.579490272149175 0.84101945570165 0.420509727850825 23 0.453100827960762 0.906201655921525 0.546899172039238 24 0.398295763542317 0.796591527084634 0.601704236457683 25 0.295997842478435 0.591995684956871 0.704002157521565 26 0.226468938381627 0.452937876763253 0.773531061618373 27 0.16875529749765 0.3375105949953 0.83124470250235 28 0.215091006110744 0.430182012221488 0.784908993889256 29 0.153246074352870 0.306492148705739 0.84675392564713 30 0.120766196519901 0.241532393039801 0.8792338034801 31 0.0890305257935383 0.178061051587077 0.910969474206462 32 0.0598377976279825 0.119675595255965 0.940162202372018 33 0.162701365282741 0.325402730565482 0.83729863471726 34 0.258165920890452 0.516331841780904 0.741834079109548 35 0.333645706474622 0.667291412949244 0.666354293525378 36 0.473623156311011 0.947246312622022 0.526376843688989 37 0.452654039260112 0.905308078520225 0.547345960739888 38 0.393627562432704 0.787255124865407 0.606372437567296 39 0.341324685574294 0.682649371148589 0.658675314425706 40 0.478786711196821 0.957573422393642 0.521213288803179 41 0.441375449563054 0.882750899126109 0.558624550436946 42 0.410868484588935 0.821736969177871 0.589131515411065 43 0.382445635036376 0.764891270072752 0.617554364963624 44 0.384559773906191 0.769119547812381 0.615440226093809 45 0.346320312132222 0.692640624264445 0.653679687867778 46 0.292232898062271 0.584465796124542 0.707767101937729 47 0.26430576074041 0.52861152148082 0.73569423925959 48 0.406058592009268 0.812117184018537 0.593941407990732 49 0.350937438920878 0.701874877841756 0.649062561079122 50 0.35263612944142 0.70527225888284 0.64736387055858 51 0.373962270196830 0.747924540393661 0.62603772980317 52 0.325267931889751 0.650535863779503 0.674732068110249 53 0.374967105249709 0.749934210499419 0.625032894750291 54 0.344189363711347 0.688378727422693 0.655810636288653 55 0.483387715533512 0.966775431067025 0.516612284466488 56 0.515353030655373 0.969293938689254 0.484646969344627 57 0.479538063411826 0.959076126823652 0.520461936588174 58 0.437027765465334 0.874055530930668 0.562972234534666 59 0.390905544835725 0.78181108967145 0.609094455164275 60 0.353160651356887 0.706321302713774 0.646839348643113 61 0.340891297374976 0.681782594749953 0.659108702625024 62 0.309817424042619 0.619634848085237 0.690182575957381 63 0.282445980731753 0.564891961463506 0.717554019268247 64 0.321488148466317 0.642976296932634 0.678511851533683 65 0.285368676766167 0.570737353532335 0.714631323233833 66 0.287060464600542 0.574120929201083 0.712939535399458 67 0.337942708461558 0.675885416923116 0.662057291538442 68 0.351191990462686 0.702383980925372 0.648808009537314 69 0.307810044093833 0.615620088187667 0.692189955906167 70 0.282188724983377 0.564377449966753 0.717811275016623 71 0.307826574755284 0.615653149510568 0.692173425244716 72 0.267457927345408 0.534915854690815 0.732542072654592 73 0.227260046688532 0.454520093377064 0.772739953311468 74 0.197169720948465 0.39433944189693 0.802830279051535 75 0.196215200310023 0.392430400620046 0.803784799689977 76 0.176078179659133 0.352156359318265 0.823921820340867 77 0.167304107711089 0.334608215422178 0.832695892288911 78 0.138376594004867 0.276753188009735 0.861623405995133 79 0.113189954357791 0.226379908715583 0.886810045642209 80 0.0939762060425675 0.187952412085135 0.906023793957432 81 0.0768987251293895 0.153797450258779 0.92310127487061 82 0.173304959345507 0.346609918691015 0.826695040654493 83 0.144972482426724 0.289944964853449 0.855027517573276 84 0.122750536609681 0.245501073219361 0.877249463390319 85 0.113143230046173 0.226286460092346 0.886856769953827 86 0.108020529073348 0.216041058146696 0.891979470926652 87 0.134450256987585 0.268900513975171 0.865549743012415 88 0.133884299338040 0.267768598676081 0.86611570066196 89 0.122142753286298 0.244285506572596 0.877857246713702 90 0.137022923668243 0.274045847336486 0.862977076331757 91 0.222345261571378 0.444690523142756 0.777654738428622 92 0.203627402308831 0.407254804617663 0.796372597691169 93 0.201465833918449 0.402931667836898 0.798534166081551 94 0.167081662161434 0.334163324322867 0.832918337838566 95 0.144713615508955 0.28942723101791 0.855286384491045 96 0.174180445582543 0.348360891165087 0.825819554417456 97 0.179381097499802 0.358762194999603 0.820618902500198 98 0.175243195000591 0.350486390001183 0.824756804999409 99 0.169689067597424 0.339378135194847 0.830310932402576 100 0.153577874386715 0.30715574877343 0.846422125613285 101 0.128265494474378 0.256530988948755 0.871734505525622 102 0.116020386141730 0.232040772283461 0.88397961385827 103 0.110888781055962 0.221777562111924 0.889111218944038 104 0.0953862111331429 0.190772422266286 0.904613788866857 105 0.119463157948182 0.238926315896364 0.880536842051818 106 0.110664351959289 0.221328703918579 0.88933564804071 107 0.101138461989134 0.202276923978269 0.898861538010866 108 0.0797927720794109 0.159585544158822 0.92020722792059 109 0.0660014108534534 0.132002821706907 0.933998589146547 110 0.06661986955296 0.13323973910592 0.93338013044704 111 0.0664026659503052 0.132805331900610 0.933597334049695 112 0.189661786981589 0.379323573963177 0.810338213018411 113 0.184156110987379 0.368312221974757 0.815843889012622 114 0.709300461453208 0.581399077093583 0.290699538546792 115 0.920716115651404 0.158567768697191 0.0792838843485956 116 0.896781829260608 0.206436341478785 0.103218170739393 117 0.932452219219889 0.135095561560223 0.0675477807801113 118 0.909532939441306 0.180934121117389 0.0904670605586943 119 0.92501658644059 0.14996682711882 0.07498341355941 120 0.94092821529941 0.118143569401182 0.0590717847005909 121 0.921645000133646 0.156709999732708 0.0783549998663538 122 0.893829439338043 0.212341121323914 0.106170560661957 123 0.973182285040207 0.0536354299195853 0.0268177149597927 124 0.958949522819624 0.0821009543607515 0.0410504771803757 125 0.942029402498017 0.115941195003966 0.0579705975019832 126 0.919290955379192 0.161418089241616 0.0807090446208081 127 0.891152243929228 0.217695512141544 0.108847756070772 128 0.856561792979405 0.286876414041189 0.143438207020595 129 0.833100649568395 0.333798700863209 0.166899350431605 130 0.795659756050792 0.408680487898416 0.204340243949208 131 0.736863007331623 0.526273985336753 0.263136992668377 132 0.666299524712448 0.667400950575103 0.333700475287552 133 0.598642025444178 0.802715949111643 0.401357974555822 134 0.519200493673635 0.96159901265273 0.480799506326365 135 0.45077478501338 0.90154957002676 0.54922521498662 136 0.38468875474502 0.76937750949004 0.61531124525498 137 0.469536621271028 0.939073242542056 0.530463378728972 138 0.657909620584822 0.684180758830356 0.342090379415178 139 0.529867836935511 0.940264326128978 0.470132163064489 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 2 0.0166666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/10qj5i1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/10qj5i1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/1t9ps1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/1t9ps1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/2t9ps1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/2t9ps1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/3t9ps1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/3t9ps1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/44iod1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/44iod1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/54iod1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/54iod1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/64iod1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/64iod1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/7f95y1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/7f95y1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/8qj5i1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/8qj5i1291052534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/9qj5i1291052534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052666yuh45sv0x0mbuvg/9qj5i1291052534.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Include Monthly 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